File size: 139,021 Bytes
1cb25d9 d0f2aec 1cb25d9 d0f2aec 1cb25d9 d0f2aec 1cb25d9 d0f2aec 1cb25d9 d0f2aec 1cb25d9 d0f2aec 1cb25d9 d0f2aec 1cb25d9 d0f2aec 1cb25d9 d0f2aec 1cb25d9 d0f2aec 1cb25d9 d0f2aec 1cb25d9 d0f2aec 1cb25d9 d0f2aec 1cb25d9 d0f2aec 1cb25d9 d0f2aec 1cb25d9 d0f2aec 1cb25d9 d0f2aec 1cb25d9 d0f2aec 1cb25d9 d0f2aec 1cb25d9 d0f2aec 1cb25d9 d0f2aec 1cb25d9 d0f2aec 1cb25d9 d0f2aec 1cb25d9 d0f2aec 1cb25d9 d0f2aec 1cb25d9 d0f2aec 1cb25d9 d0f2aec 1cb25d9 d0f2aec 1cb25d9 d0f2aec 1cb25d9 d0f2aec 1cb25d9 d0f2aec 1cb25d9 d0f2aec 1cb25d9 d0f2aec 1cb25d9 d0f2aec 1cb25d9 d0f2aec 1cb25d9 d0f2aec 1cb25d9 d0f2aec 1cb25d9 d0f2aec 1cb25d9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 2098 2099 2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112 2113 2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 2126 2127 2128 2129 2130 2131 2132 2133 2134 2135 2136 2137 |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "nwaAZRu1NTiI"
},
"source": [
"# DQN v2\n",
"\n",
"#### This version implements DQN with Keras\n",
"#### Findings:\n",
"Smaller NET then v1 does not work"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"id": "LNXxxKojNTiL"
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"from tensorflow.keras import layers\n",
"from tensorflow.keras.utils import to_categorical\n",
"import gym\n",
"from gym import spaces\n",
"from gym.utils import seeding\n",
"from gym import wrappers\n",
"\n",
"from tqdm.notebook import tqdm\n",
"from collections import deque\n",
"import numpy as np\n",
"import random\n",
"from matplotlib import pyplot as plt\n",
"from sklearn.preprocessing import MinMaxScaler\n",
"import joblib\n",
"\n",
"import io\n",
"import base64\n",
"from IPython.display import HTML, Video\n"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
"class DQN:\n",
" def __init__(self, env=None, replay_buffer_size=1000, action_size=2):\n",
" self.replay_buffer = deque(maxlen=replay_buffer_size)\n",
"\n",
" self.action_size = action_size\n",
"\n",
" # Hyperparameters\n",
" self.gamma = 0.95 # Discount rate\n",
" self.epsilon = 1.0 # Exploration rate\n",
" self.epsilon_min = 0.001 # Minimal exploration rate (epsilon-greedy)\n",
" self.epsilon_decay = 0.95 # Decay rate for epsilon\n",
" self.update_rate = 5 # Number of steps until updating the target network\n",
" self.batch_size = 100\n",
" self.learning_rate = 5e-4\n",
" \n",
" # Construct DQN models\n",
" self.model = self._build_model()\n",
" self.target_model = self._build_model()\n",
" self.target_model.set_weights(self.model.get_weights())\n",
" self.model.summary()\n",
" self.env = env\n",
" self.action_size = action_size\n",
"\n",
" self.scaler = None\n",
"\n",
" def _build_model(self):\n",
" model = tf.keras.Sequential()\n",
" \n",
" model.add(tf.keras.Input(shape=(4,)))\n",
" model.add(layers.Dense(512, activation = 'relu'))\n",
" model.add(layers.Dense(256, activation = 'relu'))\n",
" model.add(layers.Dense(128, activation = 'relu'))\n",
" model.add(layers.Dense(self.action_size, activation = 'linear'))\n",
" # model.compile(optimizer = RMSprop(lr = self.lr, rho = 0.95, epsilon = 0.01), loss = \"mse\", metrics = ['accuracy'])\n",
" \n",
" optimizer = tf.keras.optimizers.Adam(learning_rate=self.learning_rate)\n",
" # model.compile(loss='mse', optimizer=tf.keras.optimizers.RMSprop(lr = self.learning_rate, rho = 0.95, epsilon = 0.01), metrics = ['accuracy'])\n",
" model.compile(loss='mse', optimizer=optimizer, metrics = ['accuracy'])\n",
" return model\n",
"\n",
" def _min_max(self):\n",
" \"\"\"Run some steps to get data to do MINMAX scale \"\"\"\n",
" state_arr = []\n",
" state_arr.append(self.env.observation_space.high)\n",
" state_arr[0][1], state_arr[0][3] = 0,0\n",
" state_arr.append(self.env.observation_space.low)\n",
" state_arr[1][1], state_arr[1][3] = 0,0\n",
" state = self.env.reset()\n",
" for i in range(1000):\n",
" random_action = self.env.action_space.sample()\n",
" next_state, reward, done, info = self.env.step(random_action)\n",
" state_arr.append(next_state)\n",
" if done:\n",
" state = self.env.reset()\n",
"\n",
" state_arr = np.array(state_arr)\n",
" self.scaler = MinMaxScaler()\n",
" self.scaler.fit(state_arr)\n",
" # print(self.scaler.data_max_)\n",
" # print(self.scaler.data_min_)\n",
"\n",
" def _get_scaled_state(self, state):\n",
" return state\n",
" # return self.scaler.transform(state.reshape(1,-1)).flatten()\n",
"\n",
" #\n",
" # Trains the model using randomly selected experiences in the replay memory\n",
" #\n",
" def _train(self):\n",
" X, y = [], []\n",
" # state, action, reward, next_state, done \n",
" # create the targets \n",
" if self.batch_size > len(self.replay_buffer):\n",
" return\n",
" minibatch = random.sample(self.replay_buffer, self.batch_size)\n",
" mb_arr = np.array(minibatch, dtype=object)\n",
"\n",
" next_state_arr = np.stack(mb_arr[:,3])\n",
" future_qvalues = self.target_model.predict(next_state_arr, verbose=0)\n",
"\n",
" state_arr = np.stack(mb_arr[:,0])\n",
" qvalues = self.model.predict(state_arr, verbose=0)\n",
"\n",
" for index, (state, action, reward, next_state, done) in enumerate(minibatch):\n",
" if done == True:\n",
" q_target = reward\n",
" else:\n",
" q_target = reward + self.gamma * np.max(future_qvalues[index])\n",
"\n",
" q_curr = qvalues[index]\n",
" q_curr[action] = q_target \n",
" X.append(state)\n",
" y.append(q_curr)\n",
"\n",
" # Perform gradient step\n",
" X, y = np.array(X), np.array(y)\n",
" history = self.model.fit(X, y, batch_size = self.batch_size, shuffle = False, verbose=0)\n",
" # history = self.model.fit(X, y, epochs=1, verbose=0)\n",
" # print(f\"Loss: {history.history['loss']} \")\n",
"\n",
"\n",
" def learn(self, total_steps=None):\n",
" #create scaler\n",
" self._min_max()\n",
" current_episode = 0\n",
" total_reward = 0\n",
" rewards = [0]\n",
" current_step = 0\n",
" while current_step < total_steps:\n",
" current_episode += 1\n",
" state = self.env.reset()\n",
" state = self._get_scaled_state(state)\n",
" total_reward = 0\n",
" done = False\n",
" while done != True:\n",
" current_step +=1\n",
" # e-greedy\n",
" if np.random.random() > (1 - self.epsilon):\n",
" action = random.randrange(self.action_size)\n",
" else:\n",
" model_predict = self.model.predict(np.array([state]), verbose=0)\n",
" action = np.argmax(model_predict)\n",
"\n",
" # step\n",
" next_state, reward, done, info = self.env.step(action)\n",
" total_reward += reward\n",
"\n",
" next_state = self._get_scaled_state(next_state)\n",
"\n",
" # add to buffer\n",
" self.replay_buffer.append((state, action, reward, next_state, done))\n",
"\n",
" if current_step>10 and current_step % self.update_rate == 0:\n",
" print(f\"epsilon:{self.epsilon} step:{current_step} episode:{current_episode} last_score {rewards[-1]} \")\n",
" self._train()\n",
" # update target\n",
" self.target_model.set_weights(self.model.get_weights())\n",
" \n",
" state = next_state\n",
"\n",
" # update epsilon every 100 steps \n",
" if current_step % 20 == 0:\n",
" if self.epsilon > self.epsilon_min:\n",
" self.epsilon *= self.epsilon_decay\n",
"\n",
" rewards.append(total_reward)\n",
"\n",
" #\n",
" # Loads a saved model\n",
" #\n",
" def load(self, name):\n",
" self.model.load_weights(name)\n",
" self.scaler = joblib.load(name+\".scaler\") \n",
"\n",
" #\n",
" # Saves parameters of a trained model\n",
" #\n",
" def save(self, name):\n",
" self.model.save_weights(name)\n",
" joblib.dump(self.scaler, name+\".scaler\") \n",
"\n",
" def play(self, state):\n",
" state = self._get_scaled_state(state)\n",
" return np.argmax(self.model.predict(np.array([state]), verbose=0)[0])"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"sequential_36\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" dense_144 (Dense) (None, 512) 2560 \n",
" \n",
" dense_145 (Dense) (None, 256) 131328 \n",
" \n",
" dense_146 (Dense) (None, 128) 32896 \n",
" \n",
" dense_147 (Dense) (None, 2) 258 \n",
" \n",
"=================================================================\n",
"Total params: 167,042\n",
"Trainable params: 167,042\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n",
"epsilon:1.0 step:15 episode:1 last_score 0 \n",
"epsilon:1.0 step:20 episode:1 last_score 0 \n",
"epsilon:0.95 step:25 episode:1 last_score 0 \n",
"epsilon:0.95 step:30 episode:2 last_score 26.0 \n",
"epsilon:0.95 step:35 episode:2 last_score 26.0 \n",
"epsilon:0.95 step:40 episode:3 last_score 11.0 \n",
"epsilon:0.9025 step:45 episode:3 last_score 11.0 \n",
"epsilon:0.9025 step:50 episode:3 last_score 11.0 \n",
"epsilon:0.9025 step:55 episode:3 last_score 11.0 \n",
"epsilon:0.9025 step:60 episode:4 last_score 21.0 \n",
"epsilon:0.8573749999999999 step:65 episode:4 last_score 21.0 \n",
"epsilon:0.8573749999999999 step:70 episode:4 last_score 21.0 \n",
"epsilon:0.8573749999999999 step:75 episode:5 last_score 13.0 \n",
"epsilon:0.8573749999999999 step:80 episode:5 last_score 13.0 \n",
"epsilon:0.8145062499999999 step:85 episode:5 last_score 13.0 \n",
"epsilon:0.8145062499999999 step:90 episode:5 last_score 13.0 \n",
"epsilon:0.8145062499999999 step:95 episode:6 last_score 21.0 \n",
"epsilon:0.8145062499999999 step:100 episode:6 last_score 21.0 \n",
"epsilon:0.7737809374999999 step:105 episode:7 last_score 11.0 \n",
"epsilon:0.7737809374999999 step:110 episode:7 last_score 11.0 \n",
"epsilon:0.7737809374999999 step:115 episode:7 last_score 11.0 \n",
"epsilon:0.7737809374999999 step:120 episode:8 last_score 13.0 \n",
"epsilon:0.7350918906249998 step:125 episode:8 last_score 13.0 \n",
"epsilon:0.7350918906249998 step:130 episode:8 last_score 13.0 \n",
"epsilon:0.7350918906249998 step:135 episode:8 last_score 13.0 \n",
"epsilon:0.7350918906249998 step:140 episode:8 last_score 13.0 \n",
"epsilon:0.6983372960937497 step:145 episode:8 last_score 13.0 \n",
"epsilon:0.6983372960937497 step:150 episode:8 last_score 13.0 \n",
"epsilon:0.6983372960937497 step:155 episode:8 last_score 13.0 \n",
"epsilon:0.6983372960937497 step:160 episode:9 last_score 40.0 \n",
"epsilon:0.6634204312890623 step:165 episode:9 last_score 40.0 \n",
"epsilon:0.6634204312890623 step:170 episode:9 last_score 40.0 \n",
"epsilon:0.6634204312890623 step:175 episode:10 last_score 15.0 \n",
"epsilon:0.6634204312890623 step:180 episode:10 last_score 15.0 \n",
"epsilon:0.6302494097246091 step:185 episode:10 last_score 15.0 \n",
"epsilon:0.6302494097246091 step:190 episode:10 last_score 15.0 \n",
"epsilon:0.6302494097246091 step:195 episode:10 last_score 15.0 \n",
"epsilon:0.6302494097246091 step:200 episode:11 last_score 27.0 \n",
"epsilon:0.5987369392383786 step:205 episode:11 last_score 27.0 \n",
"epsilon:0.5987369392383786 step:210 episode:12 last_score 8.0 \n",
"epsilon:0.5987369392383786 step:215 episode:12 last_score 8.0 \n",
"epsilon:0.5987369392383786 step:220 episode:13 last_score 12.0 \n",
"epsilon:0.5688000922764596 step:225 episode:13 last_score 12.0 \n",
"epsilon:0.5688000922764596 step:230 episode:13 last_score 12.0 \n",
"epsilon:0.5688000922764596 step:235 episode:14 last_score 13.0 \n",
"epsilon:0.5688000922764596 step:240 episode:14 last_score 13.0 \n",
"epsilon:0.5403600876626365 step:245 episode:15 last_score 12.0 \n",
"epsilon:0.5403600876626365 step:250 episode:15 last_score 12.0 \n",
"epsilon:0.5403600876626365 step:255 episode:15 last_score 12.0 \n",
"epsilon:0.5403600876626365 step:260 episode:16 last_score 15.0 \n",
"epsilon:0.5133420832795047 step:265 episode:16 last_score 15.0 \n",
"epsilon:0.5133420832795047 step:270 episode:16 last_score 15.0 \n",
"epsilon:0.5133420832795047 step:275 episode:16 last_score 15.0 \n",
"epsilon:0.5133420832795047 step:280 episode:16 last_score 15.0 \n",
"epsilon:0.48767497911552943 step:285 episode:17 last_score 23.0 \n",
"epsilon:0.48767497911552943 step:290 episode:17 last_score 23.0 \n",
"epsilon:0.48767497911552943 step:295 episode:18 last_score 9.0 \n",
"epsilon:0.48767497911552943 step:300 episode:19 last_score 9.0 \n",
"epsilon:0.46329123015975293 step:305 episode:19 last_score 9.0 \n",
"epsilon:0.46329123015975293 step:310 episode:19 last_score 9.0 \n",
"epsilon:0.46329123015975293 step:315 episode:20 last_score 14.0 \n",
"epsilon:0.46329123015975293 step:320 episode:20 last_score 14.0 \n",
"epsilon:0.44012666865176525 step:325 episode:20 last_score 14.0 \n",
"epsilon:0.44012666865176525 step:330 episode:20 last_score 14.0 \n",
"epsilon:0.44012666865176525 step:335 episode:21 last_score 21.0 \n",
"epsilon:0.44012666865176525 step:340 episode:21 last_score 21.0 \n",
"epsilon:0.41812033521917696 step:345 episode:21 last_score 21.0 \n",
"epsilon:0.41812033521917696 step:350 episode:22 last_score 12.0 \n",
"epsilon:0.41812033521917696 step:355 episode:22 last_score 12.0 \n",
"epsilon:0.41812033521917696 step:360 episode:23 last_score 9.0 \n",
"epsilon:0.3972143184582181 step:365 episode:23 last_score 9.0 \n",
"epsilon:0.3972143184582181 step:370 episode:24 last_score 14.0 \n",
"epsilon:0.3972143184582181 step:375 episode:24 last_score 14.0 \n",
"epsilon:0.3972143184582181 step:380 episode:25 last_score 10.0 \n",
"epsilon:0.37735360253530714 step:385 episode:25 last_score 10.0 \n",
"epsilon:0.37735360253530714 step:390 episode:25 last_score 10.0 \n",
"epsilon:0.37735360253530714 step:395 episode:25 last_score 10.0 \n",
"epsilon:0.37735360253530714 step:400 episode:26 last_score 20.0 \n",
"epsilon:0.35848592240854177 step:405 episode:26 last_score 20.0 \n",
"epsilon:0.35848592240854177 step:410 episode:27 last_score 9.0 \n",
"epsilon:0.35848592240854177 step:415 episode:27 last_score 9.0 \n",
"epsilon:0.35848592240854177 step:420 episode:28 last_score 10.0 \n",
"epsilon:0.34056162628811465 step:425 episode:28 last_score 10.0 \n",
"epsilon:0.34056162628811465 step:430 episode:28 last_score 10.0 \n",
"epsilon:0.34056162628811465 step:435 episode:29 last_score 14.0 \n",
"epsilon:0.34056162628811465 step:440 episode:29 last_score 14.0 \n",
"epsilon:0.3235335449737089 step:445 episode:30 last_score 11.0 \n",
"epsilon:0.3235335449737089 step:450 episode:30 last_score 11.0 \n",
"epsilon:0.3235335449737089 step:455 episode:31 last_score 10.0 \n",
"epsilon:0.3235335449737089 step:460 episode:31 last_score 10.0 \n",
"epsilon:0.30735686772502346 step:465 episode:31 last_score 10.0 \n",
"epsilon:0.30735686772502346 step:470 episode:32 last_score 12.0 \n",
"epsilon:0.30735686772502346 step:475 episode:33 last_score 8.0 \n",
"epsilon:0.30735686772502346 step:480 episode:33 last_score 8.0 \n",
"epsilon:0.2919890243387723 step:485 episode:34 last_score 10.0 \n",
"epsilon:0.2919890243387723 step:490 episode:34 last_score 10.0 \n",
"epsilon:0.2919890243387723 step:495 episode:35 last_score 9.0 \n",
"epsilon:0.2919890243387723 step:500 episode:35 last_score 9.0 \n",
"epsilon:0.27738957312183365 step:505 episode:36 last_score 10.0 \n",
"epsilon:0.27738957312183365 step:510 episode:36 last_score 10.0 \n",
"epsilon:0.27738957312183365 step:515 episode:37 last_score 10.0 \n",
"epsilon:0.27738957312183365 step:520 episode:37 last_score 10.0 \n",
"epsilon:0.263520094465742 step:525 episode:38 last_score 9.0 \n",
"epsilon:0.263520094465742 step:530 episode:38 last_score 9.0 \n",
"epsilon:0.263520094465742 step:535 episode:39 last_score 10.0 \n",
"epsilon:0.263520094465742 step:540 episode:39 last_score 10.0 \n",
"epsilon:0.25034408974245487 step:545 episode:40 last_score 10.0 \n",
"epsilon:0.25034408974245487 step:550 episode:40 last_score 10.0 \n",
"epsilon:0.25034408974245487 step:555 episode:40 last_score 10.0 \n",
"epsilon:0.25034408974245487 step:560 episode:41 last_score 14.0 \n",
"epsilon:0.2378268852553321 step:565 episode:41 last_score 14.0 \n",
"epsilon:0.2378268852553321 step:570 episode:42 last_score 10.0 \n",
"epsilon:0.2378268852553321 step:575 episode:43 last_score 9.0 \n",
"epsilon:0.2378268852553321 step:580 episode:43 last_score 9.0 \n",
"epsilon:0.2259355409925655 step:585 episode:43 last_score 9.0 \n",
"epsilon:0.2259355409925655 step:590 episode:44 last_score 13.0 \n",
"epsilon:0.2259355409925655 step:595 episode:44 last_score 13.0 \n",
"epsilon:0.2259355409925655 step:600 episode:45 last_score 11.0 \n",
"epsilon:0.2146387639429372 step:605 episode:45 last_score 11.0 \n",
"epsilon:0.2146387639429372 step:610 episode:46 last_score 8.0 \n",
"epsilon:0.2146387639429372 step:615 episode:46 last_score 8.0 \n",
"epsilon:0.2146387639429372 step:620 episode:46 last_score 8.0 \n",
"epsilon:0.20390682574579033 step:625 episode:47 last_score 14.0 \n",
"epsilon:0.20390682574579033 step:630 episode:48 last_score 9.0 \n",
"epsilon:0.20390682574579033 step:635 episode:48 last_score 9.0 \n",
"epsilon:0.20390682574579033 step:640 episode:49 last_score 9.0 \n",
"epsilon:0.1937114844585008 step:645 episode:49 last_score 9.0 \n",
"epsilon:0.1937114844585008 step:650 episode:50 last_score 10.0 \n",
"epsilon:0.1937114844585008 step:655 episode:50 last_score 10.0 \n",
"epsilon:0.1937114844585008 step:660 episode:50 last_score 10.0 \n",
"epsilon:0.18402591023557577 step:665 episode:51 last_score 12.0 \n",
"epsilon:0.18402591023557577 step:670 episode:52 last_score 8.0 \n",
"epsilon:0.18402591023557577 step:675 episode:52 last_score 8.0 \n",
"epsilon:0.18402591023557577 step:680 episode:53 last_score 11.0 \n",
"epsilon:0.17482461472379698 step:685 episode:53 last_score 11.0 \n",
"epsilon:0.17482461472379698 step:690 episode:53 last_score 11.0 \n",
"epsilon:0.17482461472379698 step:695 episode:54 last_score 15.0 \n",
"epsilon:0.17482461472379698 step:700 episode:54 last_score 15.0 \n",
"epsilon:0.16608338398760714 step:705 episode:55 last_score 10.0 \n",
"epsilon:0.16608338398760714 step:710 episode:55 last_score 10.0 \n",
"epsilon:0.16608338398760714 step:715 episode:56 last_score 10.0 \n",
"epsilon:0.16608338398760714 step:720 episode:56 last_score 10.0 \n",
"epsilon:0.15777921478822676 step:725 episode:56 last_score 10.0 \n",
"epsilon:0.15777921478822676 step:730 episode:57 last_score 12.0 \n",
"epsilon:0.15777921478822676 step:735 episode:57 last_score 12.0 \n",
"epsilon:0.15777921478822676 step:740 episode:58 last_score 10.0 \n",
"epsilon:0.14989025404881542 step:745 episode:58 last_score 10.0 \n",
"epsilon:0.14989025404881542 step:750 episode:59 last_score 11.0 \n",
"epsilon:0.14989025404881542 step:755 episode:59 last_score 11.0 \n",
"epsilon:0.14989025404881542 step:760 episode:60 last_score 9.0 \n",
"epsilon:0.14239574134637464 step:765 episode:60 last_score 9.0 \n",
"epsilon:0.14239574134637464 step:770 episode:61 last_score 11.0 \n",
"epsilon:0.14239574134637464 step:775 episode:61 last_score 11.0 \n",
"epsilon:0.14239574134637464 step:780 episode:61 last_score 11.0 \n",
"epsilon:0.1352759542790559 step:785 episode:62 last_score 14.0 \n",
"epsilon:0.1352759542790559 step:790 episode:62 last_score 14.0 \n",
"epsilon:0.1352759542790559 step:795 episode:63 last_score 12.0 \n",
"epsilon:0.1352759542790559 step:800 episode:63 last_score 12.0 \n",
"epsilon:0.1285121565651031 step:805 episode:63 last_score 12.0 \n",
"epsilon:0.1285121565651031 step:810 episode:64 last_score 13.0 \n",
"epsilon:0.1285121565651031 step:815 episode:64 last_score 13.0 \n",
"epsilon:0.1285121565651031 step:820 episode:64 last_score 13.0 \n",
"epsilon:0.12208654873684793 step:825 episode:65 last_score 14.0 \n",
"epsilon:0.12208654873684793 step:830 episode:65 last_score 14.0 \n",
"epsilon:0.12208654873684793 step:835 episode:66 last_score 10.0 \n",
"epsilon:0.12208654873684793 step:840 episode:66 last_score 10.0 \n",
"epsilon:0.11598222130000553 step:845 episode:67 last_score 14.0 \n",
"epsilon:0.11598222130000553 step:850 episode:67 last_score 14.0 \n",
"epsilon:0.11598222130000553 step:855 episode:67 last_score 14.0 \n",
"epsilon:0.11598222130000553 step:860 episode:68 last_score 13.0 \n",
"epsilon:0.11018311023500525 step:865 episode:68 last_score 13.0 \n",
"epsilon:0.11018311023500525 step:870 episode:68 last_score 13.0 \n",
"epsilon:0.11018311023500525 step:875 episode:69 last_score 15.0 \n",
"epsilon:0.11018311023500525 step:880 episode:69 last_score 15.0 \n",
"epsilon:0.10467395472325498 step:885 episode:70 last_score 11.0 \n",
"epsilon:0.10467395472325498 step:890 episode:70 last_score 11.0 \n",
"epsilon:0.10467395472325498 step:895 episode:70 last_score 11.0 \n",
"epsilon:0.10467395472325498 step:900 episode:70 last_score 11.0 \n",
"epsilon:0.09944025698709223 step:905 episode:71 last_score 18.0 \n",
"epsilon:0.09944025698709223 step:910 episode:71 last_score 18.0 \n",
"epsilon:0.09944025698709223 step:915 episode:71 last_score 18.0 \n",
"epsilon:0.09944025698709223 step:920 episode:71 last_score 18.0 \n",
"epsilon:0.09446824413773762 step:925 episode:72 last_score 22.0 \n",
"epsilon:0.09446824413773762 step:930 episode:72 last_score 22.0 \n",
"epsilon:0.09446824413773762 step:935 episode:72 last_score 22.0 \n",
"epsilon:0.09446824413773762 step:940 episode:72 last_score 22.0 \n",
"epsilon:0.08974483193085074 step:945 episode:72 last_score 22.0 \n",
"epsilon:0.08974483193085074 step:950 episode:72 last_score 22.0 \n",
"epsilon:0.08974483193085074 step:955 episode:72 last_score 22.0 \n",
"epsilon:0.08974483193085074 step:960 episode:72 last_score 22.0 \n",
"epsilon:0.0852575903343082 step:965 episode:72 last_score 22.0 \n",
"epsilon:0.0852575903343082 step:970 episode:72 last_score 22.0 \n",
"epsilon:0.0852575903343082 step:975 episode:72 last_score 22.0 \n",
"epsilon:0.0852575903343082 step:980 episode:72 last_score 22.0 \n",
"epsilon:0.08099471081759278 step:985 episode:72 last_score 22.0 \n",
"epsilon:0.08099471081759278 step:990 episode:73 last_score 66.0 \n",
"epsilon:0.08099471081759278 step:995 episode:73 last_score 66.0 \n",
"epsilon:0.08099471081759278 step:1000 episode:73 last_score 66.0 \n",
"epsilon:0.07694497527671314 step:1005 episode:73 last_score 66.0 \n",
"epsilon:0.07694497527671314 step:1010 episode:73 last_score 66.0 \n",
"epsilon:0.07694497527671314 step:1015 episode:73 last_score 66.0 \n",
"epsilon:0.07694497527671314 step:1020 episode:73 last_score 66.0 \n",
"epsilon:0.07309772651287748 step:1025 episode:73 last_score 66.0 \n",
"epsilon:0.07309772651287748 step:1030 episode:73 last_score 66.0 \n",
"epsilon:0.07309772651287748 step:1035 episode:73 last_score 66.0 \n",
"epsilon:0.07309772651287748 step:1040 episode:73 last_score 66.0 \n",
"epsilon:0.0694428401872336 step:1045 episode:73 last_score 66.0 \n",
"epsilon:0.0694428401872336 step:1050 episode:73 last_score 66.0 \n",
"epsilon:0.0694428401872336 step:1055 episode:73 last_score 66.0 \n",
"epsilon:0.0694428401872336 step:1060 episode:73 last_score 66.0 \n",
"epsilon:0.0659706981778719 step:1065 episode:74 last_score 74.0 \n",
"epsilon:0.0659706981778719 step:1070 episode:74 last_score 74.0 \n",
"epsilon:0.0659706981778719 step:1075 episode:74 last_score 74.0 \n",
"epsilon:0.0659706981778719 step:1080 episode:74 last_score 74.0 \n",
"epsilon:0.0626721632689783 step:1085 episode:74 last_score 74.0 \n",
"epsilon:0.0626721632689783 step:1090 episode:74 last_score 74.0 \n",
"epsilon:0.0626721632689783 step:1095 episode:74 last_score 74.0 \n",
"epsilon:0.0626721632689783 step:1100 episode:74 last_score 74.0 \n",
"epsilon:0.059538555105529384 step:1105 episode:74 last_score 74.0 \n",
"epsilon:0.059538555105529384 step:1110 episode:74 last_score 74.0 \n",
"epsilon:0.059538555105529384 step:1115 episode:75 last_score 47.0 \n",
"epsilon:0.059538555105529384 step:1120 episode:75 last_score 47.0 \n",
"epsilon:0.05656162735025291 step:1125 episode:75 last_score 47.0 \n",
"epsilon:0.05656162735025291 step:1130 episode:75 last_score 47.0 \n",
"epsilon:0.05656162735025291 step:1135 episode:75 last_score 47.0 \n",
"epsilon:0.05656162735025291 step:1140 episode:75 last_score 47.0 \n",
"epsilon:0.053733545982740265 step:1145 episode:75 last_score 47.0 \n",
"epsilon:0.053733545982740265 step:1150 episode:75 last_score 47.0 \n",
"epsilon:0.053733545982740265 step:1155 episode:75 last_score 47.0 \n",
"epsilon:0.053733545982740265 step:1160 episode:75 last_score 47.0 \n",
"epsilon:0.05104686868360325 step:1165 episode:75 last_score 47.0 \n",
"epsilon:0.05104686868360325 step:1170 episode:75 last_score 47.0 \n",
"epsilon:0.05104686868360325 step:1175 episode:75 last_score 47.0 \n",
"epsilon:0.05104686868360325 step:1180 episode:75 last_score 47.0 \n",
"epsilon:0.04849452524942309 step:1185 episode:75 last_score 47.0 \n",
"epsilon:0.04849452524942309 step:1190 episode:75 last_score 47.0 \n",
"epsilon:0.04849452524942309 step:1195 episode:75 last_score 47.0 \n",
"epsilon:0.04849452524942309 step:1200 episode:75 last_score 47.0 \n",
"epsilon:0.04606979898695193 step:1205 episode:76 last_score 91.0 \n",
"epsilon:0.04606979898695193 step:1210 episode:76 last_score 91.0 \n",
"epsilon:0.04606979898695193 step:1215 episode:76 last_score 91.0 \n",
"epsilon:0.04606979898695193 step:1220 episode:76 last_score 91.0 \n",
"epsilon:0.04376630903760433 step:1225 episode:76 last_score 91.0 \n",
"epsilon:0.04376630903760433 step:1230 episode:76 last_score 91.0 \n",
"epsilon:0.04376630903760433 step:1235 episode:76 last_score 91.0 \n",
"epsilon:0.04376630903760433 step:1240 episode:76 last_score 91.0 \n",
"epsilon:0.041577993585724116 step:1245 episode:76 last_score 91.0 \n",
"epsilon:0.041577993585724116 step:1250 episode:76 last_score 91.0 \n",
"epsilon:0.041577993585724116 step:1255 episode:76 last_score 91.0 \n",
"epsilon:0.041577993585724116 step:1260 episode:76 last_score 91.0 \n",
"epsilon:0.03949909390643791 step:1265 episode:76 last_score 91.0 \n",
"epsilon:0.03949909390643791 step:1270 episode:76 last_score 91.0 \n",
"epsilon:0.03949909390643791 step:1275 episode:76 last_score 91.0 \n",
"epsilon:0.03949909390643791 step:1280 episode:76 last_score 91.0 \n",
"epsilon:0.03752413921111601 step:1285 episode:77 last_score 82.0 \n",
"epsilon:0.03752413921111601 step:1290 episode:77 last_score 82.0 \n",
"epsilon:0.03752413921111601 step:1295 episode:77 last_score 82.0 \n",
"epsilon:0.03752413921111601 step:1300 episode:77 last_score 82.0 \n",
"epsilon:0.03564793225056021 step:1305 episode:77 last_score 82.0 \n",
"epsilon:0.03564793225056021 step:1310 episode:77 last_score 82.0 \n",
"epsilon:0.03564793225056021 step:1315 episode:77 last_score 82.0 \n",
"epsilon:0.03564793225056021 step:1320 episode:77 last_score 82.0 \n",
"epsilon:0.0338655356380322 step:1325 episode:77 last_score 82.0 \n",
"epsilon:0.0338655356380322 step:1330 episode:77 last_score 82.0 \n",
"epsilon:0.0338655356380322 step:1335 episode:78 last_score 49.0 \n",
"epsilon:0.0338655356380322 step:1340 episode:78 last_score 49.0 \n",
"epsilon:0.032172258856130585 step:1345 episode:78 last_score 49.0 \n",
"epsilon:0.032172258856130585 step:1350 episode:78 last_score 49.0 \n",
"epsilon:0.032172258856130585 step:1355 episode:78 last_score 49.0 \n",
"epsilon:0.032172258856130585 step:1360 episode:78 last_score 49.0 \n",
"epsilon:0.030563645913324056 step:1365 episode:79 last_score 28.0 \n",
"epsilon:0.030563645913324056 step:1370 episode:79 last_score 28.0 \n",
"epsilon:0.030563645913324056 step:1375 episode:79 last_score 28.0 \n",
"epsilon:0.030563645913324056 step:1380 episode:79 last_score 28.0 \n",
"epsilon:0.029035463617657853 step:1385 episode:79 last_score 28.0 \n",
"epsilon:0.029035463617657853 step:1390 episode:79 last_score 28.0 \n",
"epsilon:0.029035463617657853 step:1395 episode:79 last_score 28.0 \n",
"epsilon:0.029035463617657853 step:1400 episode:80 last_score 36.0 \n",
"epsilon:0.027583690436774957 step:1405 episode:80 last_score 36.0 \n",
"epsilon:0.027583690436774957 step:1410 episode:80 last_score 36.0 \n",
"epsilon:0.027583690436774957 step:1415 episode:80 last_score 36.0 \n",
"epsilon:0.027583690436774957 step:1420 episode:80 last_score 36.0 \n",
"epsilon:0.02620450591493621 step:1425 episode:80 last_score 36.0 \n",
"epsilon:0.02620450591493621 step:1430 episode:80 last_score 36.0 \n",
"epsilon:0.02620450591493621 step:1435 episode:80 last_score 36.0 \n",
"epsilon:0.02620450591493621 step:1440 episode:80 last_score 36.0 \n",
"epsilon:0.0248942806191894 step:1445 episode:80 last_score 36.0 \n",
"epsilon:0.0248942806191894 step:1450 episode:80 last_score 36.0 \n",
"epsilon:0.0248942806191894 step:1455 episode:80 last_score 36.0 \n",
"epsilon:0.0248942806191894 step:1460 episode:81 last_score 60.0 \n",
"epsilon:0.023649566588229927 step:1465 episode:81 last_score 60.0 \n",
"epsilon:0.023649566588229927 step:1470 episode:81 last_score 60.0 \n",
"epsilon:0.023649566588229927 step:1475 episode:81 last_score 60.0 \n",
"epsilon:0.023649566588229927 step:1480 episode:81 last_score 60.0 \n",
"epsilon:0.022467088258818428 step:1485 episode:81 last_score 60.0 \n",
"epsilon:0.022467088258818428 step:1490 episode:82 last_score 32.0 \n",
"epsilon:0.022467088258818428 step:1495 episode:82 last_score 32.0 \n",
"epsilon:0.022467088258818428 step:1500 episode:82 last_score 32.0 \n",
"epsilon:0.021343733845877507 step:1505 episode:82 last_score 32.0 \n",
"epsilon:0.021343733845877507 step:1510 episode:82 last_score 32.0 \n",
"epsilon:0.021343733845877507 step:1515 episode:82 last_score 32.0 \n",
"epsilon:0.021343733845877507 step:1520 episode:82 last_score 32.0 \n",
"epsilon:0.02027654715358363 step:1525 episode:83 last_score 32.0 \n",
"epsilon:0.02027654715358363 step:1530 episode:83 last_score 32.0 \n",
"epsilon:0.02027654715358363 step:1535 episode:83 last_score 32.0 \n",
"epsilon:0.02027654715358363 step:1540 episode:83 last_score 32.0 \n",
"epsilon:0.019262719795904448 step:1545 episode:83 last_score 32.0 \n",
"epsilon:0.019262719795904448 step:1550 episode:83 last_score 32.0 \n",
"epsilon:0.019262719795904448 step:1555 episode:83 last_score 32.0 \n",
"epsilon:0.019262719795904448 step:1560 episode:83 last_score 32.0 \n",
"epsilon:0.018299583806109226 step:1565 episode:83 last_score 32.0 \n",
"epsilon:0.018299583806109226 step:1570 episode:83 last_score 32.0 \n",
"epsilon:0.018299583806109226 step:1575 episode:83 last_score 32.0 \n",
"epsilon:0.018299583806109226 step:1580 episode:84 last_score 57.0 \n",
"epsilon:0.017384604615803764 step:1585 episode:84 last_score 57.0 \n",
"epsilon:0.017384604615803764 step:1590 episode:84 last_score 57.0 \n",
"epsilon:0.017384604615803764 step:1595 episode:84 last_score 57.0 \n",
"epsilon:0.017384604615803764 step:1600 episode:84 last_score 57.0 \n",
"epsilon:0.016515374385013576 step:1605 episode:84 last_score 57.0 \n",
"epsilon:0.016515374385013576 step:1610 episode:84 last_score 57.0 \n",
"epsilon:0.016515374385013576 step:1615 episode:84 last_score 57.0 \n",
"epsilon:0.016515374385013576 step:1620 episode:84 last_score 57.0 \n",
"epsilon:0.015689605665762895 step:1625 episode:84 last_score 57.0 \n",
"epsilon:0.015689605665762895 step:1630 episode:84 last_score 57.0 \n",
"epsilon:0.015689605665762895 step:1635 episode:84 last_score 57.0 \n",
"epsilon:0.015689605665762895 step:1640 episode:84 last_score 57.0 \n",
"epsilon:0.01490512538247475 step:1645 episode:84 last_score 57.0 \n",
"epsilon:0.01490512538247475 step:1650 episode:84 last_score 57.0 \n",
"epsilon:0.01490512538247475 step:1655 episode:84 last_score 57.0 \n",
"epsilon:0.01490512538247475 step:1660 episode:84 last_score 57.0 \n",
"epsilon:0.014159869113351011 step:1665 episode:85 last_score 86.0 \n",
"epsilon:0.014159869113351011 step:1670 episode:85 last_score 86.0 \n",
"epsilon:0.014159869113351011 step:1675 episode:85 last_score 86.0 \n",
"epsilon:0.014159869113351011 step:1680 episode:85 last_score 86.0 \n",
"epsilon:0.01345187565768346 step:1685 episode:85 last_score 86.0 \n",
"epsilon:0.01345187565768346 step:1690 episode:85 last_score 86.0 \n",
"epsilon:0.01345187565768346 step:1695 episode:85 last_score 86.0 \n",
"epsilon:0.01345187565768346 step:1700 episode:85 last_score 86.0 \n",
"epsilon:0.012779281874799287 step:1705 episode:86 last_score 41.0 \n",
"epsilon:0.012779281874799287 step:1710 episode:86 last_score 41.0 \n",
"epsilon:0.012779281874799287 step:1715 episode:86 last_score 41.0 \n",
"epsilon:0.012779281874799287 step:1720 episode:86 last_score 41.0 \n",
"epsilon:0.012140317781059323 step:1725 episode:86 last_score 41.0 \n",
"epsilon:0.012140317781059323 step:1730 episode:86 last_score 41.0 \n",
"epsilon:0.012140317781059323 step:1735 episode:86 last_score 41.0 \n",
"epsilon:0.012140317781059323 step:1740 episode:86 last_score 41.0 \n",
"epsilon:0.011533301892006355 step:1745 episode:86 last_score 41.0 \n",
"epsilon:0.011533301892006355 step:1750 episode:86 last_score 41.0 \n",
"epsilon:0.011533301892006355 step:1755 episode:86 last_score 41.0 \n",
"epsilon:0.011533301892006355 step:1760 episode:86 last_score 41.0 \n",
"epsilon:0.010956636797406038 step:1765 episode:87 last_score 59.0 \n",
"epsilon:0.010956636797406038 step:1770 episode:87 last_score 59.0 \n",
"epsilon:0.010956636797406038 step:1775 episode:87 last_score 59.0 \n",
"epsilon:0.010956636797406038 step:1780 episode:87 last_score 59.0 \n",
"epsilon:0.010408804957535735 step:1785 episode:87 last_score 59.0 \n",
"epsilon:0.010408804957535735 step:1790 episode:87 last_score 59.0 \n",
"epsilon:0.010408804957535735 step:1795 episode:87 last_score 59.0 \n",
"epsilon:0.010408804957535735 step:1800 episode:87 last_score 59.0 \n",
"epsilon:0.009888364709658948 step:1805 episode:88 last_score 41.0 \n",
"epsilon:0.009888364709658948 step:1810 episode:88 last_score 41.0 \n",
"epsilon:0.009888364709658948 step:1815 episode:88 last_score 41.0 \n",
"epsilon:0.009888364709658948 step:1820 episode:88 last_score 41.0 \n",
"epsilon:0.009393946474176 step:1825 episode:88 last_score 41.0 \n",
"epsilon:0.009393946474176 step:1830 episode:88 last_score 41.0 \n",
"epsilon:0.009393946474176 step:1835 episode:88 last_score 41.0 \n",
"epsilon:0.009393946474176 step:1840 episode:89 last_score 34.0 \n",
"epsilon:0.0089242491504672 step:1845 episode:89 last_score 34.0 \n",
"epsilon:0.0089242491504672 step:1850 episode:89 last_score 34.0 \n",
"epsilon:0.0089242491504672 step:1855 episode:89 last_score 34.0 \n",
"epsilon:0.0089242491504672 step:1860 episode:89 last_score 34.0 \n",
"epsilon:0.008478036692943839 step:1865 episode:89 last_score 34.0 \n",
"epsilon:0.008478036692943839 step:1870 episode:89 last_score 34.0 \n",
"epsilon:0.008478036692943839 step:1875 episode:90 last_score 32.0 \n",
"epsilon:0.008478036692943839 step:1880 episode:90 last_score 32.0 \n",
"epsilon:0.008054134858296647 step:1885 episode:90 last_score 32.0 \n",
"epsilon:0.008054134858296647 step:1890 episode:90 last_score 32.0 \n",
"epsilon:0.008054134858296647 step:1895 episode:90 last_score 32.0 \n",
"epsilon:0.008054134858296647 step:1900 episode:90 last_score 32.0 \n",
"epsilon:0.0076514281153818135 step:1905 episode:90 last_score 32.0 \n",
"epsilon:0.0076514281153818135 step:1910 episode:90 last_score 32.0 \n",
"epsilon:0.0076514281153818135 step:1915 episode:91 last_score 44.0 \n",
"epsilon:0.0076514281153818135 step:1920 episode:91 last_score 44.0 \n",
"epsilon:0.0072688567096127225 step:1925 episode:91 last_score 44.0 \n",
"epsilon:0.0072688567096127225 step:1930 episode:91 last_score 44.0 \n",
"epsilon:0.0072688567096127225 step:1935 episode:91 last_score 44.0 \n",
"epsilon:0.0072688567096127225 step:1940 episode:91 last_score 44.0 \n",
"epsilon:0.006905413874132086 step:1945 episode:91 last_score 44.0 \n",
"epsilon:0.006905413874132086 step:1950 episode:91 last_score 44.0 \n",
"epsilon:0.006905413874132086 step:1955 episode:91 last_score 44.0 \n",
"epsilon:0.006905413874132086 step:1960 episode:91 last_score 44.0 \n",
"epsilon:0.006560143180425482 step:1965 episode:92 last_score 46.0 \n",
"epsilon:0.006560143180425482 step:1970 episode:92 last_score 46.0 \n",
"epsilon:0.006560143180425482 step:1975 episode:92 last_score 46.0 \n",
"epsilon:0.006560143180425482 step:1980 episode:92 last_score 46.0 \n",
"epsilon:0.0062321360214042075 step:1985 episode:92 last_score 46.0 \n",
"epsilon:0.0062321360214042075 step:1990 episode:92 last_score 46.0 \n",
"epsilon:0.0062321360214042075 step:1995 episode:92 last_score 46.0 \n",
"epsilon:0.0062321360214042075 step:2000 episode:92 last_score 46.0 \n",
"epsilon:0.005920529220333997 step:2005 episode:92 last_score 46.0 \n",
"epsilon:0.005920529220333997 step:2010 episode:92 last_score 46.0 \n",
"epsilon:0.005920529220333997 step:2015 episode:92 last_score 46.0 \n",
"epsilon:0.005920529220333997 step:2020 episode:92 last_score 46.0 \n",
"epsilon:0.0056245027593172965 step:2025 episode:92 last_score 46.0 \n",
"epsilon:0.0056245027593172965 step:2030 episode:92 last_score 46.0 \n",
"epsilon:0.0056245027593172965 step:2035 episode:93 last_score 71.0 \n",
"epsilon:0.0056245027593172965 step:2040 episode:93 last_score 71.0 \n",
"epsilon:0.005343277621351432 step:2045 episode:93 last_score 71.0 \n",
"epsilon:0.005343277621351432 step:2050 episode:93 last_score 71.0 \n",
"epsilon:0.005343277621351432 step:2055 episode:93 last_score 71.0 \n",
"epsilon:0.005343277621351432 step:2060 episode:93 last_score 71.0 \n",
"epsilon:0.0050761137402838595 step:2065 episode:93 last_score 71.0 \n",
"epsilon:0.0050761137402838595 step:2070 episode:93 last_score 71.0 \n",
"epsilon:0.0050761137402838595 step:2075 episode:93 last_score 71.0 \n",
"epsilon:0.0050761137402838595 step:2080 episode:93 last_score 71.0 \n",
"epsilon:0.004822308053269666 step:2085 episode:93 last_score 71.0 \n",
"epsilon:0.004822308053269666 step:2090 episode:93 last_score 71.0 \n",
"epsilon:0.004822308053269666 step:2095 episode:93 last_score 71.0 \n",
"epsilon:0.004822308053269666 step:2100 episode:93 last_score 71.0 \n",
"epsilon:0.004581192650606183 step:2105 episode:93 last_score 71.0 \n",
"epsilon:0.004581192650606183 step:2110 episode:93 last_score 71.0 \n",
"epsilon:0.004581192650606183 step:2115 episode:93 last_score 71.0 \n",
"epsilon:0.004581192650606183 step:2120 episode:93 last_score 71.0 \n",
"epsilon:0.0043521330180758735 step:2125 episode:93 last_score 71.0 \n",
"epsilon:0.0043521330180758735 step:2130 episode:93 last_score 71.0 \n",
"epsilon:0.0043521330180758735 step:2135 episode:93 last_score 71.0 \n",
"epsilon:0.0043521330180758735 step:2140 episode:93 last_score 71.0 \n",
"epsilon:0.0041345263671720795 step:2145 episode:93 last_score 71.0 \n",
"epsilon:0.0041345263671720795 step:2150 episode:93 last_score 71.0 \n",
"epsilon:0.0041345263671720795 step:2155 episode:93 last_score 71.0 \n",
"epsilon:0.0041345263671720795 step:2160 episode:93 last_score 71.0 \n",
"epsilon:0.003927800048813475 step:2165 episode:93 last_score 71.0 \n",
"epsilon:0.003927800048813475 step:2170 episode:93 last_score 71.0 \n",
"epsilon:0.003927800048813475 step:2175 episode:93 last_score 71.0 \n",
"epsilon:0.003927800048813475 step:2180 episode:94 last_score 146.0 \n",
"epsilon:0.0037314100463728015 step:2185 episode:94 last_score 146.0 \n",
"epsilon:0.0037314100463728015 step:2190 episode:94 last_score 146.0 \n",
"epsilon:0.0037314100463728015 step:2195 episode:94 last_score 146.0 \n",
"epsilon:0.0037314100463728015 step:2200 episode:94 last_score 146.0 \n",
"epsilon:0.0035448395440541612 step:2205 episode:94 last_score 146.0 \n",
"epsilon:0.0035448395440541612 step:2210 episode:94 last_score 146.0 \n",
"epsilon:0.0035448395440541612 step:2215 episode:95 last_score 35.0 \n",
"epsilon:0.0035448395440541612 step:2220 episode:95 last_score 35.0 \n",
"epsilon:0.003367597566851453 step:2225 episode:95 last_score 35.0 \n",
"epsilon:0.003367597566851453 step:2230 episode:95 last_score 35.0 \n",
"epsilon:0.003367597566851453 step:2235 episode:95 last_score 35.0 \n",
"epsilon:0.003367597566851453 step:2240 episode:95 last_score 35.0 \n",
"epsilon:0.00319921768850888 step:2245 episode:95 last_score 35.0 \n",
"epsilon:0.00319921768850888 step:2250 episode:95 last_score 35.0 \n",
"epsilon:0.00319921768850888 step:2255 episode:95 last_score 35.0 \n",
"epsilon:0.00319921768850888 step:2260 episode:96 last_score 46.0 \n",
"epsilon:0.003039256804083436 step:2265 episode:96 last_score 46.0 \n",
"epsilon:0.003039256804083436 step:2270 episode:96 last_score 46.0 \n",
"epsilon:0.003039256804083436 step:2275 episode:96 last_score 46.0 \n",
"epsilon:0.003039256804083436 step:2280 episode:96 last_score 46.0 \n",
"epsilon:0.0028872939638792637 step:2285 episode:96 last_score 46.0 \n",
"epsilon:0.0028872939638792637 step:2290 episode:96 last_score 46.0 \n",
"epsilon:0.0028872939638792637 step:2295 episode:96 last_score 46.0 \n",
"epsilon:0.0028872939638792637 step:2300 episode:97 last_score 40.0 \n",
"epsilon:0.0027429292656853004 step:2305 episode:97 last_score 40.0 \n",
"epsilon:0.0027429292656853004 step:2310 episode:97 last_score 40.0 \n",
"epsilon:0.0027429292656853004 step:2315 episode:97 last_score 40.0 \n",
"epsilon:0.0027429292656853004 step:2320 episode:97 last_score 40.0 \n",
"epsilon:0.0026057828024010354 step:2325 episode:97 last_score 40.0 \n",
"epsilon:0.0026057828024010354 step:2330 episode:97 last_score 40.0 \n",
"epsilon:0.0026057828024010354 step:2335 episode:97 last_score 40.0 \n",
"epsilon:0.0026057828024010354 step:2340 episode:97 last_score 40.0 \n",
"epsilon:0.0024754936622809836 step:2345 episode:97 last_score 40.0 \n",
"epsilon:0.0024754936622809836 step:2350 episode:97 last_score 40.0 \n",
"epsilon:0.0024754936622809836 step:2355 episode:97 last_score 40.0 \n",
"epsilon:0.0024754936622809836 step:2360 episode:97 last_score 40.0 \n",
"epsilon:0.002351718979166934 step:2365 episode:97 last_score 40.0 \n",
"epsilon:0.002351718979166934 step:2370 episode:97 last_score 40.0 \n",
"epsilon:0.002351718979166934 step:2375 episode:98 last_score 74.0 \n",
"epsilon:0.002351718979166934 step:2380 episode:98 last_score 74.0 \n",
"epsilon:0.0022341330302085875 step:2385 episode:98 last_score 74.0 \n",
"epsilon:0.0022341330302085875 step:2390 episode:98 last_score 74.0 \n",
"epsilon:0.0022341330302085875 step:2395 episode:98 last_score 74.0 \n",
"epsilon:0.0022341330302085875 step:2400 episode:98 last_score 74.0 \n",
"epsilon:0.002122426378698158 step:2405 episode:98 last_score 74.0 \n",
"epsilon:0.002122426378698158 step:2410 episode:98 last_score 74.0 \n",
"epsilon:0.002122426378698158 step:2415 episode:98 last_score 74.0 \n",
"epsilon:0.002122426378698158 step:2420 episode:98 last_score 74.0 \n",
"epsilon:0.0020163050597632503 step:2425 episode:98 last_score 74.0 \n",
"epsilon:0.0020163050597632503 step:2430 episode:98 last_score 74.0 \n",
"epsilon:0.0020163050597632503 step:2435 episode:98 last_score 74.0 \n",
"epsilon:0.0020163050597632503 step:2440 episode:98 last_score 74.0 \n",
"epsilon:0.0019154898067750877 step:2445 episode:98 last_score 74.0 \n",
"epsilon:0.0019154898067750877 step:2450 episode:98 last_score 74.0 \n",
"epsilon:0.0019154898067750877 step:2455 episode:98 last_score 74.0 \n",
"epsilon:0.0019154898067750877 step:2460 episode:98 last_score 74.0 \n",
"epsilon:0.0018197153164363333 step:2465 episode:98 last_score 74.0 \n",
"epsilon:0.0018197153164363333 step:2470 episode:98 last_score 74.0 \n",
"epsilon:0.0018197153164363333 step:2475 episode:98 last_score 74.0 \n",
"epsilon:0.0018197153164363333 step:2480 episode:99 last_score 105.0 \n",
"epsilon:0.0017287295506145165 step:2485 episode:99 last_score 105.0 \n",
"epsilon:0.0017287295506145165 step:2490 episode:99 last_score 105.0 \n",
"epsilon:0.0017287295506145165 step:2495 episode:99 last_score 105.0 \n",
"epsilon:0.0017287295506145165 step:2500 episode:99 last_score 105.0 \n",
"epsilon:0.0016422930730837905 step:2505 episode:99 last_score 105.0 \n",
"epsilon:0.0016422930730837905 step:2510 episode:99 last_score 105.0 \n",
"epsilon:0.0016422930730837905 step:2515 episode:99 last_score 105.0 \n",
"epsilon:0.0016422930730837905 step:2520 episode:99 last_score 105.0 \n",
"epsilon:0.0015601784194296008 step:2525 episode:99 last_score 105.0 \n",
"epsilon:0.0015601784194296008 step:2530 episode:99 last_score 105.0 \n",
"epsilon:0.0015601784194296008 step:2535 episode:99 last_score 105.0 \n",
"epsilon:0.0015601784194296008 step:2540 episode:99 last_score 105.0 \n",
"epsilon:0.0014821694984581207 step:2545 episode:99 last_score 105.0 \n",
"epsilon:0.0014821694984581207 step:2550 episode:99 last_score 105.0 \n",
"epsilon:0.0014821694984581207 step:2555 episode:99 last_score 105.0 \n",
"epsilon:0.0014821694984581207 step:2560 episode:100 last_score 79.0 \n",
"epsilon:0.0014080610235352145 step:2565 episode:100 last_score 79.0 \n",
"epsilon:0.0014080610235352145 step:2570 episode:100 last_score 79.0 \n",
"epsilon:0.0014080610235352145 step:2575 episode:100 last_score 79.0 \n",
"epsilon:0.0014080610235352145 step:2580 episode:100 last_score 79.0 \n",
"epsilon:0.0013376579723584536 step:2585 episode:100 last_score 79.0 \n",
"epsilon:0.0013376579723584536 step:2590 episode:100 last_score 79.0 \n",
"epsilon:0.0013376579723584536 step:2595 episode:100 last_score 79.0 \n",
"epsilon:0.0013376579723584536 step:2600 episode:100 last_score 79.0 \n",
"epsilon:0.0012707750737405309 step:2605 episode:100 last_score 79.0 \n",
"epsilon:0.0012707750737405309 step:2610 episode:100 last_score 79.0 \n",
"epsilon:0.0012707750737405309 step:2615 episode:100 last_score 79.0 \n",
"epsilon:0.0012707750737405309 step:2620 episode:100 last_score 79.0 \n",
"epsilon:0.0012072363200535043 step:2625 episode:100 last_score 79.0 \n",
"epsilon:0.0012072363200535043 step:2630 episode:100 last_score 79.0 \n",
"epsilon:0.0012072363200535043 step:2635 episode:100 last_score 79.0 \n",
"epsilon:0.0012072363200535043 step:2640 episode:100 last_score 79.0 \n",
"epsilon:0.001146874504050829 step:2645 episode:100 last_score 79.0 \n",
"epsilon:0.001146874504050829 step:2650 episode:101 last_score 92.0 \n",
"epsilon:0.001146874504050829 step:2655 episode:101 last_score 92.0 \n",
"epsilon:0.001146874504050829 step:2660 episode:101 last_score 92.0 \n",
"epsilon:0.0010895307788482875 step:2665 episode:101 last_score 92.0 \n",
"epsilon:0.0010895307788482875 step:2670 episode:101 last_score 92.0 \n",
"epsilon:0.0010895307788482875 step:2675 episode:101 last_score 92.0 \n",
"epsilon:0.0010895307788482875 step:2680 episode:101 last_score 92.0 \n",
"epsilon:0.001035054239905873 step:2685 episode:101 last_score 92.0 \n",
"epsilon:0.001035054239905873 step:2690 episode:101 last_score 92.0 \n",
"epsilon:0.001035054239905873 step:2695 episode:101 last_score 92.0 \n",
"epsilon:0.001035054239905873 step:2700 episode:101 last_score 92.0 \n",
"epsilon:0.0009833015279105794 step:2705 episode:101 last_score 92.0 \n",
"epsilon:0.0009833015279105794 step:2710 episode:102 last_score 61.0 \n",
"epsilon:0.0009833015279105794 step:2715 episode:102 last_score 61.0 \n",
"epsilon:0.0009833015279105794 step:2720 episode:102 last_score 61.0 \n",
"epsilon:0.0009833015279105794 step:2725 episode:102 last_score 61.0 \n",
"epsilon:0.0009833015279105794 step:2730 episode:102 last_score 61.0 \n",
"epsilon:0.0009833015279105794 step:2735 episode:102 last_score 61.0 \n",
"epsilon:0.0009833015279105794 step:2740 episode:102 last_score 61.0 \n",
"epsilon:0.0009833015279105794 step:2745 episode:102 last_score 61.0 \n",
"epsilon:0.0009833015279105794 step:2750 episode:102 last_score 61.0 \n",
"epsilon:0.0009833015279105794 step:2755 episode:102 last_score 61.0 \n",
"epsilon:0.0009833015279105794 step:2760 episode:102 last_score 61.0 \n",
"epsilon:0.0009833015279105794 step:2765 episode:102 last_score 61.0 \n",
"epsilon:0.0009833015279105794 step:2770 episode:102 last_score 61.0 \n",
"epsilon:0.0009833015279105794 step:2775 episode:103 last_score 64.0 \n",
"epsilon:0.0009833015279105794 step:2780 episode:103 last_score 64.0 \n",
"epsilon:0.0009833015279105794 step:2785 episode:103 last_score 64.0 \n",
"epsilon:0.0009833015279105794 step:2790 episode:103 last_score 64.0 \n",
"epsilon:0.0009833015279105794 step:2795 episode:103 last_score 64.0 \n",
"epsilon:0.0009833015279105794 step:2800 episode:103 last_score 64.0 \n",
"epsilon:0.0009833015279105794 step:2805 episode:103 last_score 64.0 \n",
"epsilon:0.0009833015279105794 step:2810 episode:103 last_score 64.0 \n",
"epsilon:0.0009833015279105794 step:2815 episode:103 last_score 64.0 \n",
"epsilon:0.0009833015279105794 step:2820 episode:103 last_score 64.0 \n",
"epsilon:0.0009833015279105794 step:2825 episode:103 last_score 64.0 \n",
"epsilon:0.0009833015279105794 step:2830 episode:103 last_score 64.0 \n",
"epsilon:0.0009833015279105794 step:2835 episode:103 last_score 64.0 \n",
"epsilon:0.0009833015279105794 step:2840 episode:103 last_score 64.0 \n",
"epsilon:0.0009833015279105794 step:2845 episode:103 last_score 64.0 \n",
"epsilon:0.0009833015279105794 step:2850 episode:103 last_score 64.0 \n",
"epsilon:0.0009833015279105794 step:2855 episode:103 last_score 64.0 \n",
"epsilon:0.0009833015279105794 step:2860 episode:103 last_score 64.0 \n",
"epsilon:0.0009833015279105794 step:2865 episode:103 last_score 64.0 \n",
"epsilon:0.0009833015279105794 step:2870 episode:103 last_score 64.0 \n",
"epsilon:0.0009833015279105794 step:2875 episode:103 last_score 64.0 \n",
"epsilon:0.0009833015279105794 step:2880 episode:103 last_score 64.0 \n",
"epsilon:0.0009833015279105794 step:2885 episode:103 last_score 64.0 \n",
"epsilon:0.0009833015279105794 step:2890 episode:103 last_score 64.0 \n",
"epsilon:0.0009833015279105794 step:2895 episode:103 last_score 64.0 \n",
"epsilon:0.0009833015279105794 step:2900 episode:103 last_score 64.0 \n",
"epsilon:0.0009833015279105794 step:2905 episode:103 last_score 64.0 \n",
"epsilon:0.0009833015279105794 step:2910 episode:103 last_score 64.0 \n",
"epsilon:0.0009833015279105794 step:2915 episode:103 last_score 64.0 \n",
"epsilon:0.0009833015279105794 step:2920 episode:103 last_score 64.0 \n",
"epsilon:0.0009833015279105794 step:2925 episode:103 last_score 64.0 \n",
"epsilon:0.0009833015279105794 step:2930 episode:104 last_score 153.0 \n",
"epsilon:0.0009833015279105794 step:2935 episode:104 last_score 153.0 \n",
"epsilon:0.0009833015279105794 step:2940 episode:104 last_score 153.0 \n",
"epsilon:0.0009833015279105794 step:2945 episode:104 last_score 153.0 \n",
"epsilon:0.0009833015279105794 step:2950 episode:104 last_score 153.0 \n",
"epsilon:0.0009833015279105794 step:2955 episode:104 last_score 153.0 \n",
"epsilon:0.0009833015279105794 step:2960 episode:104 last_score 153.0 \n",
"epsilon:0.0009833015279105794 step:2965 episode:104 last_score 153.0 \n",
"epsilon:0.0009833015279105794 step:2970 episode:104 last_score 153.0 \n",
"epsilon:0.0009833015279105794 step:2975 episode:104 last_score 153.0 \n",
"epsilon:0.0009833015279105794 step:2980 episode:104 last_score 153.0 \n",
"epsilon:0.0009833015279105794 step:2985 episode:104 last_score 153.0 \n",
"epsilon:0.0009833015279105794 step:2990 episode:104 last_score 153.0 \n",
"epsilon:0.0009833015279105794 step:2995 episode:104 last_score 153.0 \n",
"epsilon:0.0009833015279105794 step:3000 episode:104 last_score 153.0 \n",
"epsilon:0.0009833015279105794 step:3005 episode:104 last_score 153.0 \n",
"epsilon:0.0009833015279105794 step:3010 episode:105 last_score 82.0 \n",
"epsilon:0.0009833015279105794 step:3015 episode:105 last_score 82.0 \n",
"epsilon:0.0009833015279105794 step:3020 episode:105 last_score 82.0 \n",
"epsilon:0.0009833015279105794 step:3025 episode:105 last_score 82.0 \n",
"epsilon:0.0009833015279105794 step:3030 episode:105 last_score 82.0 \n",
"epsilon:0.0009833015279105794 step:3035 episode:105 last_score 82.0 \n",
"epsilon:0.0009833015279105794 step:3040 episode:105 last_score 82.0 \n",
"epsilon:0.0009833015279105794 step:3045 episode:105 last_score 82.0 \n",
"epsilon:0.0009833015279105794 step:3050 episode:105 last_score 82.0 \n",
"epsilon:0.0009833015279105794 step:3055 episode:105 last_score 82.0 \n",
"epsilon:0.0009833015279105794 step:3060 episode:105 last_score 82.0 \n",
"epsilon:0.0009833015279105794 step:3065 episode:105 last_score 82.0 \n",
"epsilon:0.0009833015279105794 step:3070 episode:105 last_score 82.0 \n",
"epsilon:0.0009833015279105794 step:3075 episode:105 last_score 82.0 \n",
"epsilon:0.0009833015279105794 step:3080 episode:105 last_score 82.0 \n",
"epsilon:0.0009833015279105794 step:3085 episode:105 last_score 82.0 \n",
"epsilon:0.0009833015279105794 step:3090 episode:105 last_score 82.0 \n",
"epsilon:0.0009833015279105794 step:3095 episode:105 last_score 82.0 \n",
"epsilon:0.0009833015279105794 step:3100 episode:105 last_score 82.0 \n",
"epsilon:0.0009833015279105794 step:3105 episode:106 last_score 96.0 \n",
"epsilon:0.0009833015279105794 step:3110 episode:106 last_score 96.0 \n",
"epsilon:0.0009833015279105794 step:3115 episode:106 last_score 96.0 \n",
"epsilon:0.0009833015279105794 step:3120 episode:106 last_score 96.0 \n",
"epsilon:0.0009833015279105794 step:3125 episode:106 last_score 96.0 \n",
"epsilon:0.0009833015279105794 step:3130 episode:106 last_score 96.0 \n",
"epsilon:0.0009833015279105794 step:3135 episode:106 last_score 96.0 \n",
"epsilon:0.0009833015279105794 step:3140 episode:106 last_score 96.0 \n",
"epsilon:0.0009833015279105794 step:3145 episode:106 last_score 96.0 \n",
"epsilon:0.0009833015279105794 step:3150 episode:106 last_score 96.0 \n",
"epsilon:0.0009833015279105794 step:3155 episode:106 last_score 96.0 \n",
"epsilon:0.0009833015279105794 step:3160 episode:106 last_score 96.0 \n",
"epsilon:0.0009833015279105794 step:3165 episode:106 last_score 96.0 \n",
"epsilon:0.0009833015279105794 step:3170 episode:106 last_score 96.0 \n",
"epsilon:0.0009833015279105794 step:3175 episode:106 last_score 96.0 \n",
"epsilon:0.0009833015279105794 step:3180 episode:106 last_score 96.0 \n",
"epsilon:0.0009833015279105794 step:3185 episode:106 last_score 96.0 \n",
"epsilon:0.0009833015279105794 step:3190 episode:106 last_score 96.0 \n",
"epsilon:0.0009833015279105794 step:3195 episode:106 last_score 96.0 \n",
"epsilon:0.0009833015279105794 step:3200 episode:106 last_score 96.0 \n",
"epsilon:0.0009833015279105794 step:3205 episode:106 last_score 96.0 \n",
"epsilon:0.0009833015279105794 step:3210 episode:106 last_score 96.0 \n",
"epsilon:0.0009833015279105794 step:3215 episode:106 last_score 96.0 \n",
"epsilon:0.0009833015279105794 step:3220 episode:106 last_score 96.0 \n",
"epsilon:0.0009833015279105794 step:3225 episode:106 last_score 96.0 \n",
"epsilon:0.0009833015279105794 step:3230 episode:107 last_score 121.0 \n",
"epsilon:0.0009833015279105794 step:3235 episode:107 last_score 121.0 \n",
"epsilon:0.0009833015279105794 step:3240 episode:107 last_score 121.0 \n",
"epsilon:0.0009833015279105794 step:3245 episode:107 last_score 121.0 \n",
"epsilon:0.0009833015279105794 step:3250 episode:107 last_score 121.0 \n",
"epsilon:0.0009833015279105794 step:3255 episode:107 last_score 121.0 \n",
"epsilon:0.0009833015279105794 step:3260 episode:107 last_score 121.0 \n",
"epsilon:0.0009833015279105794 step:3265 episode:107 last_score 121.0 \n",
"epsilon:0.0009833015279105794 step:3270 episode:107 last_score 121.0 \n",
"epsilon:0.0009833015279105794 step:3275 episode:107 last_score 121.0 \n",
"epsilon:0.0009833015279105794 step:3280 episode:107 last_score 121.0 \n",
"epsilon:0.0009833015279105794 step:3285 episode:107 last_score 121.0 \n",
"epsilon:0.0009833015279105794 step:3290 episode:107 last_score 121.0 \n",
"epsilon:0.0009833015279105794 step:3295 episode:107 last_score 121.0 \n",
"epsilon:0.0009833015279105794 step:3300 episode:107 last_score 121.0 \n",
"epsilon:0.0009833015279105794 step:3305 episode:107 last_score 121.0 \n",
"epsilon:0.0009833015279105794 step:3310 episode:107 last_score 121.0 \n",
"epsilon:0.0009833015279105794 step:3315 episode:107 last_score 121.0 \n",
"epsilon:0.0009833015279105794 step:3320 episode:107 last_score 121.0 \n",
"epsilon:0.0009833015279105794 step:3325 episode:107 last_score 121.0 \n",
"epsilon:0.0009833015279105794 step:3330 episode:107 last_score 121.0 \n",
"epsilon:0.0009833015279105794 step:3335 episode:107 last_score 121.0 \n",
"epsilon:0.0009833015279105794 step:3340 episode:107 last_score 121.0 \n",
"epsilon:0.0009833015279105794 step:3345 episode:107 last_score 121.0 \n",
"epsilon:0.0009833015279105794 step:3350 episode:107 last_score 121.0 \n",
"epsilon:0.0009833015279105794 step:3355 episode:107 last_score 121.0 \n",
"epsilon:0.0009833015279105794 step:3360 episode:107 last_score 121.0 \n",
"epsilon:0.0009833015279105794 step:3365 episode:107 last_score 121.0 \n",
"epsilon:0.0009833015279105794 step:3370 episode:107 last_score 121.0 \n",
"epsilon:0.0009833015279105794 step:3375 episode:107 last_score 121.0 \n",
"epsilon:0.0009833015279105794 step:3380 episode:107 last_score 121.0 \n",
"epsilon:0.0009833015279105794 step:3385 episode:107 last_score 121.0 \n",
"epsilon:0.0009833015279105794 step:3390 episode:107 last_score 121.0 \n",
"epsilon:0.0009833015279105794 step:3395 episode:107 last_score 121.0 \n",
"epsilon:0.0009833015279105794 step:3400 episode:107 last_score 121.0 \n",
"epsilon:0.0009833015279105794 step:3405 episode:107 last_score 121.0 \n",
"epsilon:0.0009833015279105794 step:3410 episode:107 last_score 121.0 \n",
"epsilon:0.0009833015279105794 step:3415 episode:107 last_score 121.0 \n",
"epsilon:0.0009833015279105794 step:3420 episode:107 last_score 121.0 \n",
"epsilon:0.0009833015279105794 step:3425 episode:107 last_score 121.0 \n",
"epsilon:0.0009833015279105794 step:3430 episode:107 last_score 121.0 \n",
"epsilon:0.0009833015279105794 step:3435 episode:107 last_score 121.0 \n",
"epsilon:0.0009833015279105794 step:3440 episode:107 last_score 121.0 \n",
"epsilon:0.0009833015279105794 step:3445 episode:107 last_score 121.0 \n",
"epsilon:0.0009833015279105794 step:3450 episode:107 last_score 121.0 \n",
"epsilon:0.0009833015279105794 step:3455 episode:107 last_score 121.0 \n",
"epsilon:0.0009833015279105794 step:3460 episode:108 last_score 234.0 \n",
"epsilon:0.0009833015279105794 step:3465 episode:108 last_score 234.0 \n",
"epsilon:0.0009833015279105794 step:3470 episode:108 last_score 234.0 \n",
"epsilon:0.0009833015279105794 step:3475 episode:108 last_score 234.0 \n",
"epsilon:0.0009833015279105794 step:3480 episode:108 last_score 234.0 \n",
"epsilon:0.0009833015279105794 step:3485 episode:108 last_score 234.0 \n",
"epsilon:0.0009833015279105794 step:3490 episode:108 last_score 234.0 \n",
"epsilon:0.0009833015279105794 step:3495 episode:108 last_score 234.0 \n",
"epsilon:0.0009833015279105794 step:3500 episode:108 last_score 234.0 \n",
"epsilon:0.0009833015279105794 step:3505 episode:108 last_score 234.0 \n",
"epsilon:0.0009833015279105794 step:3510 episode:108 last_score 234.0 \n",
"epsilon:0.0009833015279105794 step:3515 episode:108 last_score 234.0 \n",
"epsilon:0.0009833015279105794 step:3520 episode:108 last_score 234.0 \n",
"epsilon:0.0009833015279105794 step:3525 episode:108 last_score 234.0 \n",
"epsilon:0.0009833015279105794 step:3530 episode:108 last_score 234.0 \n",
"epsilon:0.0009833015279105794 step:3535 episode:108 last_score 234.0 \n",
"epsilon:0.0009833015279105794 step:3540 episode:108 last_score 234.0 \n",
"epsilon:0.0009833015279105794 step:3545 episode:108 last_score 234.0 \n",
"epsilon:0.0009833015279105794 step:3550 episode:108 last_score 234.0 \n",
"epsilon:0.0009833015279105794 step:3555 episode:108 last_score 234.0 \n",
"epsilon:0.0009833015279105794 step:3560 episode:108 last_score 234.0 \n",
"epsilon:0.0009833015279105794 step:3565 episode:108 last_score 234.0 \n",
"epsilon:0.0009833015279105794 step:3570 episode:108 last_score 234.0 \n",
"epsilon:0.0009833015279105794 step:3575 episode:108 last_score 234.0 \n",
"epsilon:0.0009833015279105794 step:3580 episode:109 last_score 118.0 \n",
"epsilon:0.0009833015279105794 step:3585 episode:109 last_score 118.0 \n",
"epsilon:0.0009833015279105794 step:3590 episode:109 last_score 118.0 \n",
"epsilon:0.0009833015279105794 step:3595 episode:109 last_score 118.0 \n",
"epsilon:0.0009833015279105794 step:3600 episode:109 last_score 118.0 \n",
"epsilon:0.0009833015279105794 step:3605 episode:109 last_score 118.0 \n",
"epsilon:0.0009833015279105794 step:3610 episode:109 last_score 118.0 \n",
"epsilon:0.0009833015279105794 step:3615 episode:109 last_score 118.0 \n",
"epsilon:0.0009833015279105794 step:3620 episode:109 last_score 118.0 \n",
"epsilon:0.0009833015279105794 step:3625 episode:109 last_score 118.0 \n",
"epsilon:0.0009833015279105794 step:3630 episode:109 last_score 118.0 \n",
"epsilon:0.0009833015279105794 step:3635 episode:109 last_score 118.0 \n",
"epsilon:0.0009833015279105794 step:3640 episode:109 last_score 118.0 \n",
"epsilon:0.0009833015279105794 step:3645 episode:109 last_score 118.0 \n",
"epsilon:0.0009833015279105794 step:3650 episode:109 last_score 118.0 \n",
"epsilon:0.0009833015279105794 step:3655 episode:109 last_score 118.0 \n",
"epsilon:0.0009833015279105794 step:3660 episode:109 last_score 118.0 \n",
"epsilon:0.0009833015279105794 step:3665 episode:109 last_score 118.0 \n",
"epsilon:0.0009833015279105794 step:3670 episode:109 last_score 118.0 \n",
"epsilon:0.0009833015279105794 step:3675 episode:109 last_score 118.0 \n",
"epsilon:0.0009833015279105794 step:3680 episode:109 last_score 118.0 \n",
"epsilon:0.0009833015279105794 step:3685 episode:109 last_score 118.0 \n",
"epsilon:0.0009833015279105794 step:3690 episode:109 last_score 118.0 \n",
"epsilon:0.0009833015279105794 step:3695 episode:109 last_score 118.0 \n",
"epsilon:0.0009833015279105794 step:3700 episode:109 last_score 118.0 \n",
"epsilon:0.0009833015279105794 step:3705 episode:109 last_score 118.0 \n",
"epsilon:0.0009833015279105794 step:3710 episode:109 last_score 118.0 \n",
"epsilon:0.0009833015279105794 step:3715 episode:109 last_score 118.0 \n",
"epsilon:0.0009833015279105794 step:3720 episode:109 last_score 118.0 \n",
"epsilon:0.0009833015279105794 step:3725 episode:110 last_score 143.0 \n",
"epsilon:0.0009833015279105794 step:3730 episode:110 last_score 143.0 \n",
"epsilon:0.0009833015279105794 step:3735 episode:110 last_score 143.0 \n",
"epsilon:0.0009833015279105794 step:3740 episode:110 last_score 143.0 \n",
"epsilon:0.0009833015279105794 step:3745 episode:110 last_score 143.0 \n",
"epsilon:0.0009833015279105794 step:3750 episode:110 last_score 143.0 \n",
"epsilon:0.0009833015279105794 step:3755 episode:110 last_score 143.0 \n",
"epsilon:0.0009833015279105794 step:3760 episode:110 last_score 143.0 \n",
"epsilon:0.0009833015279105794 step:3765 episode:110 last_score 143.0 \n",
"epsilon:0.0009833015279105794 step:3770 episode:110 last_score 143.0 \n",
"epsilon:0.0009833015279105794 step:3775 episode:110 last_score 143.0 \n",
"epsilon:0.0009833015279105794 step:3780 episode:110 last_score 143.0 \n",
"epsilon:0.0009833015279105794 step:3785 episode:110 last_score 143.0 \n",
"epsilon:0.0009833015279105794 step:3790 episode:110 last_score 143.0 \n",
"epsilon:0.0009833015279105794 step:3795 episode:110 last_score 143.0 \n",
"epsilon:0.0009833015279105794 step:3800 episode:110 last_score 143.0 \n",
"epsilon:0.0009833015279105794 step:3805 episode:110 last_score 143.0 \n",
"epsilon:0.0009833015279105794 step:3810 episode:110 last_score 143.0 \n",
"epsilon:0.0009833015279105794 step:3815 episode:110 last_score 143.0 \n",
"epsilon:0.0009833015279105794 step:3820 episode:110 last_score 143.0 \n",
"epsilon:0.0009833015279105794 step:3825 episode:110 last_score 143.0 \n",
"epsilon:0.0009833015279105794 step:3830 episode:110 last_score 143.0 \n",
"epsilon:0.0009833015279105794 step:3835 episode:110 last_score 143.0 \n",
"epsilon:0.0009833015279105794 step:3840 episode:110 last_score 143.0 \n",
"epsilon:0.0009833015279105794 step:3845 episode:110 last_score 143.0 \n",
"epsilon:0.0009833015279105794 step:3850 episode:110 last_score 143.0 \n",
"epsilon:0.0009833015279105794 step:3855 episode:111 last_score 131.0 \n",
"epsilon:0.0009833015279105794 step:3860 episode:111 last_score 131.0 \n",
"epsilon:0.0009833015279105794 step:3865 episode:111 last_score 131.0 \n",
"epsilon:0.0009833015279105794 step:3870 episode:111 last_score 131.0 \n",
"epsilon:0.0009833015279105794 step:3875 episode:111 last_score 131.0 \n",
"epsilon:0.0009833015279105794 step:3880 episode:111 last_score 131.0 \n",
"epsilon:0.0009833015279105794 step:3885 episode:111 last_score 131.0 \n",
"epsilon:0.0009833015279105794 step:3890 episode:111 last_score 131.0 \n",
"epsilon:0.0009833015279105794 step:3895 episode:111 last_score 131.0 \n",
"epsilon:0.0009833015279105794 step:3900 episode:111 last_score 131.0 \n",
"epsilon:0.0009833015279105794 step:3905 episode:111 last_score 131.0 \n",
"epsilon:0.0009833015279105794 step:3910 episode:111 last_score 131.0 \n",
"epsilon:0.0009833015279105794 step:3915 episode:111 last_score 131.0 \n",
"epsilon:0.0009833015279105794 step:3920 episode:111 last_score 131.0 \n",
"epsilon:0.0009833015279105794 step:3925 episode:111 last_score 131.0 \n",
"epsilon:0.0009833015279105794 step:3930 episode:111 last_score 131.0 \n",
"epsilon:0.0009833015279105794 step:3935 episode:111 last_score 131.0 \n",
"epsilon:0.0009833015279105794 step:3940 episode:111 last_score 131.0 \n",
"epsilon:0.0009833015279105794 step:3945 episode:111 last_score 131.0 \n",
"epsilon:0.0009833015279105794 step:3950 episode:111 last_score 131.0 \n",
"epsilon:0.0009833015279105794 step:3955 episode:111 last_score 131.0 \n",
"epsilon:0.0009833015279105794 step:3960 episode:111 last_score 131.0 \n",
"epsilon:0.0009833015279105794 step:3965 episode:111 last_score 131.0 \n",
"epsilon:0.0009833015279105794 step:3970 episode:111 last_score 131.0 \n",
"epsilon:0.0009833015279105794 step:3975 episode:111 last_score 131.0 \n",
"epsilon:0.0009833015279105794 step:3980 episode:111 last_score 131.0 \n",
"epsilon:0.0009833015279105794 step:3985 episode:111 last_score 131.0 \n",
"epsilon:0.0009833015279105794 step:3990 episode:112 last_score 137.0 \n",
"epsilon:0.0009833015279105794 step:3995 episode:112 last_score 137.0 \n",
"epsilon:0.0009833015279105794 step:4000 episode:112 last_score 137.0 \n",
"epsilon:0.0009833015279105794 step:4005 episode:112 last_score 137.0 \n",
"epsilon:0.0009833015279105794 step:4010 episode:112 last_score 137.0 \n",
"epsilon:0.0009833015279105794 step:4015 episode:112 last_score 137.0 \n",
"epsilon:0.0009833015279105794 step:4020 episode:112 last_score 137.0 \n",
"epsilon:0.0009833015279105794 step:4025 episode:112 last_score 137.0 \n",
"epsilon:0.0009833015279105794 step:4030 episode:112 last_score 137.0 \n",
"epsilon:0.0009833015279105794 step:4035 episode:112 last_score 137.0 \n",
"epsilon:0.0009833015279105794 step:4040 episode:112 last_score 137.0 \n",
"epsilon:0.0009833015279105794 step:4045 episode:112 last_score 137.0 \n",
"epsilon:0.0009833015279105794 step:4050 episode:112 last_score 137.0 \n",
"epsilon:0.0009833015279105794 step:4055 episode:112 last_score 137.0 \n",
"epsilon:0.0009833015279105794 step:4060 episode:112 last_score 137.0 \n",
"epsilon:0.0009833015279105794 step:4065 episode:112 last_score 137.0 \n",
"epsilon:0.0009833015279105794 step:4070 episode:112 last_score 137.0 \n",
"epsilon:0.0009833015279105794 step:4075 episode:112 last_score 137.0 \n",
"epsilon:0.0009833015279105794 step:4080 episode:112 last_score 137.0 \n",
"epsilon:0.0009833015279105794 step:4085 episode:112 last_score 137.0 \n",
"epsilon:0.0009833015279105794 step:4090 episode:112 last_score 137.0 \n",
"epsilon:0.0009833015279105794 step:4095 episode:112 last_score 137.0 \n",
"epsilon:0.0009833015279105794 step:4100 episode:112 last_score 137.0 \n",
"epsilon:0.0009833015279105794 step:4105 episode:112 last_score 137.0 \n",
"epsilon:0.0009833015279105794 step:4110 episode:112 last_score 137.0 \n",
"epsilon:0.0009833015279105794 step:4115 episode:112 last_score 137.0 \n",
"epsilon:0.0009833015279105794 step:4120 episode:112 last_score 137.0 \n",
"epsilon:0.0009833015279105794 step:4125 episode:112 last_score 137.0 \n",
"epsilon:0.0009833015279105794 step:4130 episode:112 last_score 137.0 \n",
"epsilon:0.0009833015279105794 step:4135 episode:112 last_score 137.0 \n",
"epsilon:0.0009833015279105794 step:4140 episode:112 last_score 137.0 \n",
"epsilon:0.0009833015279105794 step:4145 episode:112 last_score 137.0 \n",
"epsilon:0.0009833015279105794 step:4150 episode:112 last_score 137.0 \n",
"epsilon:0.0009833015279105794 step:4155 episode:112 last_score 137.0 \n",
"epsilon:0.0009833015279105794 step:4160 episode:112 last_score 137.0 \n",
"epsilon:0.0009833015279105794 step:4165 episode:112 last_score 137.0 \n",
"epsilon:0.0009833015279105794 step:4170 episode:112 last_score 137.0 \n",
"epsilon:0.0009833015279105794 step:4175 episode:112 last_score 137.0 \n",
"epsilon:0.0009833015279105794 step:4180 episode:112 last_score 137.0 \n",
"epsilon:0.0009833015279105794 step:4185 episode:112 last_score 137.0 \n",
"epsilon:0.0009833015279105794 step:4190 episode:113 last_score 199.0 \n",
"epsilon:0.0009833015279105794 step:4195 episode:113 last_score 199.0 \n",
"epsilon:0.0009833015279105794 step:4200 episode:113 last_score 199.0 \n",
"epsilon:0.0009833015279105794 step:4205 episode:113 last_score 199.0 \n",
"epsilon:0.0009833015279105794 step:4210 episode:113 last_score 199.0 \n",
"epsilon:0.0009833015279105794 step:4215 episode:113 last_score 199.0 \n",
"epsilon:0.0009833015279105794 step:4220 episode:113 last_score 199.0 \n",
"epsilon:0.0009833015279105794 step:4225 episode:113 last_score 199.0 \n",
"epsilon:0.0009833015279105794 step:4230 episode:113 last_score 199.0 \n",
"epsilon:0.0009833015279105794 step:4235 episode:113 last_score 199.0 \n",
"epsilon:0.0009833015279105794 step:4240 episode:113 last_score 199.0 \n",
"epsilon:0.0009833015279105794 step:4245 episode:113 last_score 199.0 \n",
"epsilon:0.0009833015279105794 step:4250 episode:113 last_score 199.0 \n",
"epsilon:0.0009833015279105794 step:4255 episode:113 last_score 199.0 \n",
"epsilon:0.0009833015279105794 step:4260 episode:113 last_score 199.0 \n",
"epsilon:0.0009833015279105794 step:4265 episode:113 last_score 199.0 \n",
"epsilon:0.0009833015279105794 step:4270 episode:113 last_score 199.0 \n",
"epsilon:0.0009833015279105794 step:4275 episode:113 last_score 199.0 \n",
"epsilon:0.0009833015279105794 step:4280 episode:113 last_score 199.0 \n",
"epsilon:0.0009833015279105794 step:4285 episode:113 last_score 199.0 \n",
"epsilon:0.0009833015279105794 step:4290 episode:113 last_score 199.0 \n",
"epsilon:0.0009833015279105794 step:4295 episode:113 last_score 199.0 \n",
"epsilon:0.0009833015279105794 step:4300 episode:113 last_score 199.0 \n",
"epsilon:0.0009833015279105794 step:4305 episode:113 last_score 199.0 \n",
"epsilon:0.0009833015279105794 step:4310 episode:113 last_score 199.0 \n",
"epsilon:0.0009833015279105794 step:4315 episode:113 last_score 199.0 \n",
"epsilon:0.0009833015279105794 step:4320 episode:113 last_score 199.0 \n",
"epsilon:0.0009833015279105794 step:4325 episode:113 last_score 199.0 \n",
"epsilon:0.0009833015279105794 step:4330 episode:114 last_score 139.0 \n",
"epsilon:0.0009833015279105794 step:4335 episode:114 last_score 139.0 \n",
"epsilon:0.0009833015279105794 step:4340 episode:114 last_score 139.0 \n",
"epsilon:0.0009833015279105794 step:4345 episode:114 last_score 139.0 \n",
"epsilon:0.0009833015279105794 step:4350 episode:114 last_score 139.0 \n",
"epsilon:0.0009833015279105794 step:4355 episode:114 last_score 139.0 \n",
"epsilon:0.0009833015279105794 step:4360 episode:114 last_score 139.0 \n",
"epsilon:0.0009833015279105794 step:4365 episode:114 last_score 139.0 \n",
"epsilon:0.0009833015279105794 step:4370 episode:114 last_score 139.0 \n",
"epsilon:0.0009833015279105794 step:4375 episode:114 last_score 139.0 \n",
"epsilon:0.0009833015279105794 step:4380 episode:114 last_score 139.0 \n",
"epsilon:0.0009833015279105794 step:4385 episode:114 last_score 139.0 \n",
"epsilon:0.0009833015279105794 step:4390 episode:114 last_score 139.0 \n",
"epsilon:0.0009833015279105794 step:4395 episode:114 last_score 139.0 \n",
"epsilon:0.0009833015279105794 step:4400 episode:114 last_score 139.0 \n",
"epsilon:0.0009833015279105794 step:4405 episode:114 last_score 139.0 \n",
"epsilon:0.0009833015279105794 step:4410 episode:114 last_score 139.0 \n",
"epsilon:0.0009833015279105794 step:4415 episode:114 last_score 139.0 \n",
"epsilon:0.0009833015279105794 step:4420 episode:114 last_score 139.0 \n",
"epsilon:0.0009833015279105794 step:4425 episode:114 last_score 139.0 \n",
"epsilon:0.0009833015279105794 step:4430 episode:114 last_score 139.0 \n",
"epsilon:0.0009833015279105794 step:4435 episode:114 last_score 139.0 \n",
"epsilon:0.0009833015279105794 step:4440 episode:114 last_score 139.0 \n",
"epsilon:0.0009833015279105794 step:4445 episode:114 last_score 139.0 \n",
"epsilon:0.0009833015279105794 step:4450 episode:114 last_score 139.0 \n",
"epsilon:0.0009833015279105794 step:4455 episode:114 last_score 139.0 \n",
"epsilon:0.0009833015279105794 step:4460 episode:114 last_score 139.0 \n",
"epsilon:0.0009833015279105794 step:4465 episode:114 last_score 139.0 \n",
"epsilon:0.0009833015279105794 step:4470 episode:114 last_score 139.0 \n",
"epsilon:0.0009833015279105794 step:4475 episode:114 last_score 139.0 \n",
"epsilon:0.0009833015279105794 step:4480 episode:114 last_score 139.0 \n",
"epsilon:0.0009833015279105794 step:4485 episode:114 last_score 139.0 \n",
"epsilon:0.0009833015279105794 step:4490 episode:115 last_score 160.0 \n",
"epsilon:0.0009833015279105794 step:4495 episode:115 last_score 160.0 \n",
"epsilon:0.0009833015279105794 step:4500 episode:115 last_score 160.0 \n",
"epsilon:0.0009833015279105794 step:4505 episode:115 last_score 160.0 \n",
"epsilon:0.0009833015279105794 step:4510 episode:115 last_score 160.0 \n",
"epsilon:0.0009833015279105794 step:4515 episode:115 last_score 160.0 \n",
"epsilon:0.0009833015279105794 step:4520 episode:115 last_score 160.0 \n",
"epsilon:0.0009833015279105794 step:4525 episode:115 last_score 160.0 \n",
"epsilon:0.0009833015279105794 step:4530 episode:115 last_score 160.0 \n",
"epsilon:0.0009833015279105794 step:4535 episode:115 last_score 160.0 \n",
"epsilon:0.0009833015279105794 step:4540 episode:115 last_score 160.0 \n",
"epsilon:0.0009833015279105794 step:4545 episode:115 last_score 160.0 \n",
"epsilon:0.0009833015279105794 step:4550 episode:115 last_score 160.0 \n",
"epsilon:0.0009833015279105794 step:4555 episode:115 last_score 160.0 \n",
"epsilon:0.0009833015279105794 step:4560 episode:115 last_score 160.0 \n",
"epsilon:0.0009833015279105794 step:4565 episode:115 last_score 160.0 \n",
"epsilon:0.0009833015279105794 step:4570 episode:115 last_score 160.0 \n",
"epsilon:0.0009833015279105794 step:4575 episode:115 last_score 160.0 \n",
"epsilon:0.0009833015279105794 step:4580 episode:115 last_score 160.0 \n",
"epsilon:0.0009833015279105794 step:4585 episode:115 last_score 160.0 \n",
"epsilon:0.0009833015279105794 step:4590 episode:115 last_score 160.0 \n",
"epsilon:0.0009833015279105794 step:4595 episode:115 last_score 160.0 \n",
"epsilon:0.0009833015279105794 step:4600 episode:115 last_score 160.0 \n",
"epsilon:0.0009833015279105794 step:4605 episode:115 last_score 160.0 \n",
"epsilon:0.0009833015279105794 step:4610 episode:115 last_score 160.0 \n",
"epsilon:0.0009833015279105794 step:4615 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4620 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4625 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4630 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4635 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4640 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4645 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4650 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4655 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4660 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4665 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4670 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4675 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4680 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4685 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4690 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4695 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4700 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4705 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4710 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4715 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4720 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4725 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4730 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4735 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4740 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4745 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4750 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4755 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4760 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4765 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4770 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4775 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4780 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4785 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4790 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4795 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4800 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4805 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4810 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4815 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4820 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4825 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4830 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4835 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4840 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4845 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4850 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4855 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4860 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4865 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4870 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4875 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4880 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4885 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4890 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4895 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4900 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4905 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4910 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4915 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4920 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4925 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4930 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4935 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4940 episode:116 last_score 125.0 \n",
"epsilon:0.0009833015279105794 step:4945 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:4950 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:4955 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:4960 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:4965 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:4970 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:4975 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:4980 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:4985 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:4990 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:4995 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5000 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5005 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5010 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5015 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5020 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5025 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5030 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5035 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5040 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5045 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5050 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5055 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5060 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5065 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5070 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5075 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5080 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5085 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5090 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5095 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5100 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5105 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5110 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5115 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5120 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5125 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5130 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5135 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5140 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5145 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5150 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5155 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5160 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5165 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5170 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5175 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5180 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5185 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5190 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5195 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5200 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5205 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5210 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5215 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5220 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5225 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5230 episode:117 last_score 330.0 \n",
"epsilon:0.0009833015279105794 step:5235 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5240 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5245 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5250 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5255 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5260 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5265 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5270 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5275 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5280 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5285 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5290 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5295 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5300 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5305 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5310 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5315 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5320 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5325 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5330 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5335 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5340 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5345 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5350 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5355 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5360 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5365 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5370 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5375 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5380 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5385 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5390 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5395 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5400 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5405 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5410 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5415 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5420 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5425 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5430 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5435 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5440 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5445 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5450 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5455 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5460 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5465 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5470 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5475 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5480 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5485 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5490 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5495 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5500 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5505 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5510 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5515 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5520 episode:118 last_score 292.0 \n",
"epsilon:0.0009833015279105794 step:5525 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5530 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5535 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5540 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5545 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5550 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5555 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5560 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5565 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5570 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5575 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5580 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5585 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5590 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5595 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5600 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5605 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5610 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5615 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5620 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5625 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5630 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5635 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5640 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5645 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5650 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5655 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5660 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5665 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5670 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5675 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5680 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5685 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5690 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5695 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5700 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5705 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5710 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5715 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5720 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5725 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5730 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5735 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5740 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5745 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5750 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5755 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5760 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5765 episode:119 last_score 288.0 \n",
"epsilon:0.0009833015279105794 step:5770 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:5775 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:5780 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:5785 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:5790 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:5795 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:5800 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:5805 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:5810 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:5815 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:5820 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:5825 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:5830 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:5835 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:5840 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:5845 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:5850 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:5855 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:5860 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:5865 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:5870 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:5875 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:5880 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:5885 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:5890 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:5895 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:5900 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:5905 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:5910 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:5915 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:5920 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:5925 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:5930 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:5935 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:5940 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:5945 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:5950 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:5955 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:5960 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:5965 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:5970 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:5975 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:5980 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:5985 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:5990 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:5995 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:6000 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:6005 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:6010 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:6015 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:6020 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:6025 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:6030 episode:120 last_score 244.0 \n",
"epsilon:0.0009833015279105794 step:6035 episode:120 last_score 244.0 \n"
]
}
],
"source": [
"env = gym.make('CartPole-v1')\n",
"\n",
"model = DQN(env=env, replay_buffer_size=10_000, action_size=2)\n",
"model.learn(total_steps=6_000)\n",
"env.close()"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [],
"source": [
"model.save(\"./alt/DQN_v2.h5\")"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"sequential_38\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" dense_152 (Dense) (None, 512) 2560 \n",
" \n",
" dense_153 (Dense) (None, 256) 131328 \n",
" \n",
" dense_154 (Dense) (None, 128) 32896 \n",
" \n",
" dense_155 (Dense) (None, 2) 258 \n",
" \n",
"=================================================================\n",
"Total params: 167,042\n",
"Trainable params: 167,042\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n",
"Total reward 439.0\n"
]
}
],
"source": [
"eval_env = gym.make('CartPole-v1')\n",
"model = DQN(env=eval_env, replay_buffer_size=10_000, action_size=2)\n",
"model.load(\"./alt/DQN_v2.h5\")\n",
"eval_env = wrappers.Monitor(eval_env, \"./alt/gym-results\", force=True)\n",
"state = eval_env.reset()\n",
"total_reward = 0\n",
"for _ in range(1000):\n",
" action = model.play(state)\n",
" observation, reward, done, info = eval_env.step(action)\n",
" total_reward +=reward\n",
" state = observation\n",
" if done: \n",
" break\n",
"print(f\"Total reward {total_reward}\")\n",
"eval_env.close()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 4.8000002e+00 0.0000000e+00 4.1887903e-01 0.0000000e+00]\n",
" [-4.8000002e+00 0.0000000e+00 -4.1887903e-01 0.0000000e+00]\n",
" [-4.5489166e-03 -2.4116948e-01 -4.1077949e-02 3.0230245e-01]\n",
" ...\n",
" [ 5.0607350e-02 1.7593256e-01 -8.8660561e-02 -4.2944443e-01]\n",
" [ 5.4126002e-02 3.7219086e-01 -9.7249448e-02 -7.4870765e-01]\n",
" [ 6.1569817e-02 5.6851023e-01 -1.1222360e-01 -1.0703416e+00]]\n",
"Max [4.8 2.2215695 0.41887903 2.1663814 ]\n",
"Min [-4.8 -1.4063605 -0.41887903 -2.5751066 ]\n",
"[ 0.0172858 0.00332159 -0.03439883 0.01944862]\n",
"[0.5018006 0.38856372 0.45893943 0.54720277]\n"
]
}
],
"source": [
"\n",
"# def _min_max(env):\n",
"# \"\"\"Run some steps to get data to do MINMAX scale \"\"\"\n",
"# state_arr = []\n",
"# state_arr.append(env.observation_space.high)\n",
"# state_arr[0][1], state_arr[0][3] = 0,0\n",
"# state_arr.append(env.observation_space.low)\n",
"# state_arr[1][1], state_arr[1][3] = 0,0\n",
"# state = env.reset()\n",
"# for i in range(1000):\n",
"# random_action = env.action_space.sample()\n",
"# next_state, reward, done, info = env.step(random_action)\n",
"# state_arr.append(next_state)\n",
"# if done:\n",
"# state = env.reset()\n",
"\n",
"# state_arr = np.array(state_arr)\n",
"\n",
"# print(state_arr)\n",
"# scaler = MinMaxScaler()\n",
"# scaler.fit(state_arr)\n",
"# print(\"Max \",scaler.data_max_)\n",
"# print(\"Min \", scaler.data_min_)\n",
"# return scaler\n",
"\n",
"# env = gym.make('CartPole-v1')\n",
"# scaler = _min_max(env)\n",
"# state = env.reset()\n",
"# print(state)\n",
"# state = scaler.transform(state.reshape(1, -1)).flatten()\n",
"# print(state)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": "Python 3.8.13 ('rl2')",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.13"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "cd60ab8388a66026f336166410d6a8a46ddf65ece2e85ad2d46c8b98d87580d1"
}
},
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"01a2dbcb714e40148b41c761fcf43147": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"20b0f38ec3234ff28a62a286cd57b933": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "PasswordModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "PasswordModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "PasswordView",
"continuous_update": true,
"description": "Token:",
"description_tooltip": null,
"disabled": false,
"layout": "IPY_MODEL_01a2dbcb714e40148b41c761fcf43147",
"placeholder": "",
"style": "IPY_MODEL_90c874e91b304ee1a7ef147767ac00ce",
"value": ""
}
},
"270cbb5d6e9c4b1e9e2f39c8b3b0c15f": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "VBoxModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "VBoxModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "VBoxView",
"box_style": "",
"children": [
"IPY_MODEL_a02224a43d8d4af3bd31d326540d25da",
"IPY_MODEL_20b0f38ec3234ff28a62a286cd57b933",
"IPY_MODEL_f6c845330d6743c0b35c2c7ad834de77",
"IPY_MODEL_f1675c09d16a4251b403f9c56255f168",
"IPY_MODEL_c1a82965ae26479a98e4fdbde1e64ec2"
],
"layout": "IPY_MODEL_3fa248114ac24656ba74923936a94d2d"
}
},
"2dc5fa9aa3334dfcbdee9c238f2ef60b": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"3e753b0212644990b558c68853ff2041": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"3fa248114ac24656ba74923936a94d2d": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": "center",
"align_self": null,
"border": null,
"bottom": null,
"display": "flex",
"flex": null,
"flex_flow": "column",
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": "50%"
}
},
"42d140b838b844819bc127afc1b7bc84": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"90c874e91b304ee1a7ef147767ac00ce": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"9d847f9a7d47458d8cd57d9b599e47c6": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"a02224a43d8d4af3bd31d326540d25da": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HTMLModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_caef095934ec47bbb8b64eab22049284",
"placeholder": "",
"style": "IPY_MODEL_2dc5fa9aa3334dfcbdee9c238f2ef60b",
"value": "<center> <img\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.svg\nalt='Hugging Face'> <br> Copy a token from <a\nhref=\"https://huggingface.co/settings/tokens\" target=\"_blank\">your Hugging Face\ntokens page</a> and paste it below. <br> Immediately click login after copying\nyour token or it might be stored in plain text in this notebook file. </center>"
}
},
"a2cfb91cf66447d7899292854bd64a07": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"c1a82965ae26479a98e4fdbde1e64ec2": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HTMLModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_9d847f9a7d47458d8cd57d9b599e47c6",
"placeholder": "",
"style": "IPY_MODEL_42d140b838b844819bc127afc1b7bc84",
"value": "\n<b>Pro Tip:</b> If you don't already have one, you can create a dedicated\n'notebooks' token with 'write' access, that you can then easily reuse for all\nnotebooks. </center>"
}
},
"caef095934ec47bbb8b64eab22049284": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"eaba3f1de4444aabadfea2a3dadb1d80": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"ee4a21bedc504171ad09d205d634b528": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "ButtonStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "ButtonStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"button_color": null,
"font_weight": ""
}
},
"f1675c09d16a4251b403f9c56255f168": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "ButtonModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "ButtonModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "ButtonView",
"button_style": "",
"description": "Login",
"disabled": false,
"icon": "",
"layout": "IPY_MODEL_a2cfb91cf66447d7899292854bd64a07",
"style": "IPY_MODEL_ee4a21bedc504171ad09d205d634b528",
"tooltip": ""
}
},
"f6c845330d6743c0b35c2c7ad834de77": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "CheckboxModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "CheckboxModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "CheckboxView",
"description": "Add token as git credential?",
"description_tooltip": null,
"disabled": false,
"indent": true,
"layout": "IPY_MODEL_3e753b0212644990b558c68853ff2041",
"style": "IPY_MODEL_eaba3f1de4444aabadfea2a3dadb1d80",
"value": true
}
}
}
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|