kraken2404
commited on
Commit
•
70baa16
1
Parent(s):
c4c0903
Initial commit
Browse files- README.md +1 -1
- a2c-PandaReachDense-v2.zip +2 -2
- a2c-PandaReachDense-v2/data +13 -13
- a2c-PandaReachDense-v2/policy.optimizer.pth +1 -1
- a2c-PandaReachDense-v2/policy.pth +1 -1
- config.json +1 -1
- replay.mp4 +0 -0
- results.json +1 -1
- vec_normalize.pkl +1 -1
README.md
CHANGED
@@ -16,7 +16,7 @@ model-index:
|
|
16 |
type: PandaReachDense-v2
|
17 |
metrics:
|
18 |
- type: mean_reward
|
19 |
-
value: -
|
20 |
name: mean_reward
|
21 |
verified: false
|
22 |
---
|
|
|
16 |
type: PandaReachDense-v2
|
17 |
metrics:
|
18 |
- type: mean_reward
|
19 |
+
value: -3.30 +/- 0.85
|
20 |
name: mean_reward
|
21 |
verified: false
|
22 |
---
|
a2c-PandaReachDense-v2.zip
CHANGED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
-
size
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:231368d6459cacd40c5edea94c32ec0a7ba0133627de9d4b63030f281d17978a
|
3 |
+
size 108124
|
a2c-PandaReachDense-v2/data
CHANGED
@@ -4,9 +4,9 @@
|
|
4 |
":serialized:": "gAWVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=",
|
5 |
"__module__": "stable_baselines3.common.policies",
|
6 |
"__doc__": "\n MultiInputActorClass policy class for actor-critic algorithms (has both policy and value prediction).\n Used by A2C, PPO and the likes.\n\n :param observation_space: Observation space (Tuple)\n :param action_space: Action space\n :param lr_schedule: Learning rate schedule (could be constant)\n :param net_arch: The specification of the policy and value networks.\n :param activation_fn: Activation function\n :param ortho_init: Whether to use or not orthogonal initialization\n :param use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param full_std: Whether to use (n_features x n_actions) parameters\n for the std instead of only (n_features,) when using gSDE\n :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n a positive standard deviation (cf paper). It allows to keep variance\n above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n :param squash_output: Whether to squash the output using a tanh function,\n this allows to ensure boundaries when using gSDE.\n :param features_extractor_class: Uses the CombinedExtractor\n :param features_extractor_kwargs: Keyword arguments\n to pass to the features extractor.\n :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n :param normalize_images: Whether to normalize images or not,\n dividing by 255.0 (True by default)\n :param optimizer_class: The optimizer to use,\n ``th.optim.Adam`` by default\n :param optimizer_kwargs: Additional keyword arguments,\n excluding the learning rate, to pass to the optimizer\n ",
|
7 |
-
"__init__": "<function MultiInputActorCriticPolicy.__init__ at
|
8 |
"__abstractmethods__": "frozenset()",
|
9 |
-
"_abc_impl": "<_abc._abc_data object at
|
10 |
},
|
11 |
"verbose": 1,
|
12 |
"policy_kwargs": {
|
@@ -19,12 +19,12 @@
|
|
19 |
"weight_decay": 0
|
20 |
}
|
21 |
},
|
22 |
-
"num_timesteps":
|
23 |
-
"_total_timesteps":
|
24 |
"_num_timesteps_at_start": 0,
|
25 |
"seed": null,
|
26 |
"action_noise": null,
|
27 |
-
"start_time":
|
28 |
"learning_rate": 0.0007,
|
29 |
"tensorboard_log": null,
|
30 |
"lr_schedule": {
|
@@ -33,10 +33,10 @@
|
|
33 |
},
|
34 |
"_last_obs": {
|
35 |
":type:": "<class 'collections.OrderedDict'>",
|
36 |
-
":serialized:": "
|
37 |
-
"achieved_goal": "[[0.
|
38 |
-
"desired_goal": "[[
|
39 |
-
"observation": "[[
|
40 |
},
|
41 |
"_last_episode_starts": {
|
42 |
":type:": "<class 'numpy.ndarray'>",
|
@@ -44,9 +44,9 @@
|
|
44 |
},
|
45 |
"_last_original_obs": {
|
46 |
":type:": "<class 'collections.OrderedDict'>",
|
47 |
-
":serialized:": "gAWVuwEAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QoljAAAAAAAAAA6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////
|
48 |
"achieved_goal": "[[ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]]",
|
49 |
-
"desired_goal": "[[-0.
|
50 |
"observation": "[[ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]]"
|
51 |
},
|
52 |
"_episode_num": 0,
|
@@ -56,13 +56,13 @@
|
|
56 |
"_stats_window_size": 100,
|
57 |
"ep_info_buffer": {
|
58 |
":type:": "<class 'collections.deque'>",
|
59 |
-
":serialized:": "gAWVHRAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////
|
60 |
},
|
61 |
"ep_success_buffer": {
|
62 |
":type:": "<class 'collections.deque'>",
|
63 |
":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
|
64 |
},
|
65 |
-
"_n_updates":
|
66 |
"n_steps": 5,
|
67 |
"gamma": 0.99,
|
68 |
"gae_lambda": 1.0,
|
|
|
4 |
":serialized:": "gAWVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=",
|
5 |
"__module__": "stable_baselines3.common.policies",
|
6 |
"__doc__": "\n MultiInputActorClass policy class for actor-critic algorithms (has both policy and value prediction).\n Used by A2C, PPO and the likes.\n\n :param observation_space: Observation space (Tuple)\n :param action_space: Action space\n :param lr_schedule: Learning rate schedule (could be constant)\n :param net_arch: The specification of the policy and value networks.\n :param activation_fn: Activation function\n :param ortho_init: Whether to use or not orthogonal initialization\n :param use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param full_std: Whether to use (n_features x n_actions) parameters\n for the std instead of only (n_features,) when using gSDE\n :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n a positive standard deviation (cf paper). It allows to keep variance\n above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n :param squash_output: Whether to squash the output using a tanh function,\n this allows to ensure boundaries when using gSDE.\n :param features_extractor_class: Uses the CombinedExtractor\n :param features_extractor_kwargs: Keyword arguments\n to pass to the features extractor.\n :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n :param normalize_images: Whether to normalize images or not,\n dividing by 255.0 (True by default)\n :param optimizer_class: The optimizer to use,\n ``th.optim.Adam`` by default\n :param optimizer_kwargs: Additional keyword arguments,\n excluding the learning rate, to pass to the optimizer\n ",
|
7 |
+
"__init__": "<function MultiInputActorCriticPolicy.__init__ at 0x7ff13463caf0>",
|
8 |
"__abstractmethods__": "frozenset()",
|
9 |
+
"_abc_impl": "<_abc._abc_data object at 0x7ff134636780>"
|
10 |
},
|
11 |
"verbose": 1,
|
12 |
"policy_kwargs": {
|
|
|
19 |
"weight_decay": 0
|
20 |
}
|
21 |
},
|
22 |
+
"num_timesteps": 3000000,
|
23 |
+
"_total_timesteps": 3000000,
|
24 |
"_num_timesteps_at_start": 0,
|
25 |
"seed": null,
|
26 |
"action_noise": null,
|
27 |
+
"start_time": 1683468026144647155,
|
28 |
"learning_rate": 0.0007,
|
29 |
"tensorboard_log": null,
|
30 |
"lr_schedule": {
|
|
|
33 |
},
|
34 |
"_last_obs": {
|
35 |
":type:": "<class 'collections.OrderedDict'>",
|
36 |
+
":serialized:": "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",
|
37 |
+
"achieved_goal": "[[0.35955083 0.0293235 0.6227952 ]\n [0.35955083 0.0293235 0.6227952 ]\n [0.35955083 0.0293235 0.6227952 ]\n [0.35955083 0.0293235 0.6227952 ]]",
|
38 |
+
"desired_goal": "[[ 0.859451 1.4900229 0.9344197 ]\n [-1.3853761 -0.08209857 0.42143568]\n [-0.3314207 -1.3077465 -0.69323736]\n [-1.3940917 0.17230368 -1.020856 ]]",
|
39 |
+
"observation": "[[3.5955083e-01 2.9323498e-02 6.2279522e-01 1.7972662e-03 3.1885202e-04\n 1.0841941e-02]\n [3.5955083e-01 2.9323498e-02 6.2279522e-01 1.7972662e-03 3.1885202e-04\n 1.0841941e-02]\n [3.5955083e-01 2.9323498e-02 6.2279522e-01 1.7972662e-03 3.1885202e-04\n 1.0841941e-02]\n [3.5955083e-01 2.9323498e-02 6.2279522e-01 1.7972662e-03 3.1885202e-04\n 1.0841941e-02]]"
|
40 |
},
|
41 |
"_last_episode_starts": {
|
42 |
":type:": "<class 'numpy.ndarray'>",
|
|
|
44 |
},
|
45 |
"_last_original_obs": {
|
46 |
":type:": "<class 'collections.OrderedDict'>",
|
47 |
+
":serialized:": "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",
|
48 |
"achieved_goal": "[[ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]]",
|
49 |
+
"desired_goal": "[[-0.05249646 0.01299154 0.2867825 ]\n [ 0.02886016 0.00495029 0.12135126]\n [ 0.01369878 0.09271478 0.27610585]\n [-0.05237988 0.04079446 0.25723192]]",
|
50 |
"observation": "[[ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]]"
|
51 |
},
|
52 |
"_episode_num": 0,
|
|
|
56 |
"_stats_window_size": 100,
|
57 |
"ep_info_buffer": {
|
58 |
":type:": "<class 'collections.deque'>",
|
59 |
+
":serialized:": "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"
|
60 |
},
|
61 |
"ep_success_buffer": {
|
62 |
":type:": "<class 'collections.deque'>",
|
63 |
":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
|
64 |
},
|
65 |
+
"_n_updates": 150000,
|
66 |
"n_steps": 5,
|
67 |
"gamma": 0.99,
|
68 |
"gae_lambda": 1.0,
|
a2c-PandaReachDense-v2/policy.optimizer.pth
CHANGED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
size 44734
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:546261c9a9d6fa1a6bf688a78eadd0693af161dae04f36c62ae5a514b0713da8
|
3 |
size 44734
|
a2c-PandaReachDense-v2/policy.pth
CHANGED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
size 46014
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:d8be6687cc14e27e4e4dbea0b7585bfb7c06cd28928548f75b52ff5626b7f7a3
|
3 |
size 46014
|
config.json
CHANGED
@@ -1 +1 @@
|
|
1 |
-
{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=", "__module__": "stable_baselines3.common.policies", "__doc__": "\n MultiInputActorClass policy class for actor-critic algorithms (has both policy and value prediction).\n Used by A2C, PPO and the likes.\n\n :param observation_space: Observation space (Tuple)\n :param action_space: Action space\n :param lr_schedule: Learning rate schedule (could be constant)\n :param net_arch: The specification of the policy and value networks.\n :param activation_fn: Activation function\n :param ortho_init: Whether to use or not orthogonal initialization\n :param use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param full_std: Whether to use (n_features x n_actions) parameters\n for the std instead of only (n_features,) when using gSDE\n :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n a positive standard deviation (cf paper). It allows to keep variance\n above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n :param squash_output: Whether to squash the output using a tanh function,\n this allows to ensure boundaries when using gSDE.\n :param features_extractor_class: Uses the CombinedExtractor\n :param features_extractor_kwargs: Keyword arguments\n to pass to the features extractor.\n :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n :param normalize_images: Whether to normalize images or not,\n dividing by 255.0 (True by default)\n :param optimizer_class: The optimizer to use,\n ``th.optim.Adam`` by default\n :param optimizer_kwargs: Additional keyword arguments,\n excluding the learning rate, to pass to the optimizer\n ", "__init__": "<function MultiInputActorCriticPolicy.__init__ at 0x7fc6ea1c8af0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fc6ea1bb740>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVgQAAAAAAAAB9lCiMD29wdGltaXplcl9jbGFzc5SME3RvcmNoLm9wdGltLnJtc3Byb3CUjAdSTVNwcm9wlJOUjBBvcHRpbWl6ZXJfa3dhcmdzlH2UKIwFYWxwaGGURz/vrhR64UeujANlcHOURz7k+LWI42jxjAx3ZWlnaHRfZGVjYXmUSwB1dS4=", "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>", "optimizer_kwargs": {"alpha": 0.99, "eps": 1e-05, "weight_decay": 0}}, "num_timesteps": 2000000, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1682946948753469252, "learning_rate": 0.0007, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuCQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz9G8AaNuLrHhZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[0.6479091 0.07563881 0.6133304 ]\n [0.6479091 0.07563881 0.6133304 ]\n [0.6479091 0.07563881 0.6133304 ]\n [0.6479091 0.07563881 0.6133304 ]]", "desired_goal": "[[-0.62952435 -1.5563589 -1.389532 ]\n [-0.9855095 1.1244037 1.0938382 ]\n [-1.0674878 -0.8647763 -0.27649006]\n [ 1.0978241 0.86338687 -0.5781316 ]]", "observation": "[[ 0.6479091 0.07563881 0.6133304 0.04383769 -0.00199246 0.01192959]\n [ 0.6479091 0.07563881 0.6133304 0.04383769 -0.00199246 0.01192959]\n [ 0.6479091 0.07563881 0.6133304 0.04383769 -0.00199246 0.01192959]\n [ 0.6479091 0.07563881 0.6133304 0.04383769 -0.00199246 0.01192959]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]]", "desired_goal": "[[-0.08931521 0.06045492 0.07171407]\n [ 0.02356035 -0.04864237 0.11368653]\n [ 0.02197638 -0.04892383 0.12518767]\n [ 0.14123191 -0.11169082 0.15776171]]", "observation": "[[ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]]"}, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": 0.0, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 100000, "n_steps": 5, "gamma": 0.99, "gae_lambda": 1.0, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "normalize_advantage": false, "observation_space": {":type:": "<class 'gym.spaces.dict.Dict'>", ":serialized:": "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", "spaces": "OrderedDict([('achieved_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('desired_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('observation', Box([-10. -10. -10. -10. -10. -10.], [10. 10. 10. 10. 10. 10.], (6,), float32))])", "_shape": null, "dtype": null, "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [3], "low": "[-1. -1. -1.]", "high": "[1. 1. 1.]", "bounded_below": "[ True True True]", "bounded_above": "[ True True True]", "_np_random": null}, "n_envs": 4, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.10.11", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.0+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}
|
|
|
1 |
+
{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=", "__module__": "stable_baselines3.common.policies", "__doc__": "\n MultiInputActorClass policy class for actor-critic algorithms (has both policy and value prediction).\n Used by A2C, PPO and the likes.\n\n :param observation_space: Observation space (Tuple)\n :param action_space: Action space\n :param lr_schedule: Learning rate schedule (could be constant)\n :param net_arch: The specification of the policy and value networks.\n :param activation_fn: Activation function\n :param ortho_init: Whether to use or not orthogonal initialization\n :param use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param full_std: Whether to use (n_features x n_actions) parameters\n for the std instead of only (n_features,) when using gSDE\n :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n a positive standard deviation (cf paper). It allows to keep variance\n above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n :param squash_output: Whether to squash the output using a tanh function,\n this allows to ensure boundaries when using gSDE.\n :param features_extractor_class: Uses the CombinedExtractor\n :param features_extractor_kwargs: Keyword arguments\n to pass to the features extractor.\n :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n :param normalize_images: Whether to normalize images or not,\n dividing by 255.0 (True by default)\n :param optimizer_class: The optimizer to use,\n ``th.optim.Adam`` by default\n :param optimizer_kwargs: Additional keyword arguments,\n excluding the learning rate, to pass to the optimizer\n ", "__init__": "<function MultiInputActorCriticPolicy.__init__ at 0x7ff13463caf0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7ff134636780>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVgQAAAAAAAAB9lCiMD29wdGltaXplcl9jbGFzc5SME3RvcmNoLm9wdGltLnJtc3Byb3CUjAdSTVNwcm9wlJOUjBBvcHRpbWl6ZXJfa3dhcmdzlH2UKIwFYWxwaGGURz/vrhR64UeujANlcHOURz7k+LWI42jxjAx3ZWlnaHRfZGVjYXmUSwB1dS4=", "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>", "optimizer_kwargs": {"alpha": 0.99, "eps": 1e-05, "weight_decay": 0}}, "num_timesteps": 3000000, "_total_timesteps": 3000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1683468026144647155, "learning_rate": 0.0007, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[0.35955083 0.0293235 0.6227952 ]\n [0.35955083 0.0293235 0.6227952 ]\n [0.35955083 0.0293235 0.6227952 ]\n [0.35955083 0.0293235 0.6227952 ]]", "desired_goal": "[[ 0.859451 1.4900229 0.9344197 ]\n [-1.3853761 -0.08209857 0.42143568]\n [-0.3314207 -1.3077465 -0.69323736]\n [-1.3940917 0.17230368 -1.020856 ]]", "observation": "[[3.5955083e-01 2.9323498e-02 6.2279522e-01 1.7972662e-03 3.1885202e-04\n 1.0841941e-02]\n [3.5955083e-01 2.9323498e-02 6.2279522e-01 1.7972662e-03 3.1885202e-04\n 1.0841941e-02]\n [3.5955083e-01 2.9323498e-02 6.2279522e-01 1.7972662e-03 3.1885202e-04\n 1.0841941e-02]\n [3.5955083e-01 2.9323498e-02 6.2279522e-01 1.7972662e-03 3.1885202e-04\n 1.0841941e-02]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]]", "desired_goal": "[[-0.05249646 0.01299154 0.2867825 ]\n [ 0.02886016 0.00495029 0.12135126]\n [ 0.01369878 0.09271478 0.27610585]\n [-0.05237988 0.04079446 0.25723192]]", "observation": "[[ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]]"}, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": 0.0, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 150000, "n_steps": 5, "gamma": 0.99, "gae_lambda": 1.0, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "normalize_advantage": false, "observation_space": {":type:": "<class 'gym.spaces.dict.Dict'>", ":serialized:": "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", "spaces": "OrderedDict([('achieved_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('desired_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('observation', Box([-10. -10. -10. -10. -10. -10.], [10. 10. 10. 10. 10. 10.], (6,), float32))])", "_shape": null, "dtype": null, "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [3], "low": "[-1. -1. -1.]", "high": "[1. 1. 1.]", "bounded_below": "[ True True True]", "bounded_above": "[ True True True]", "_np_random": null}, "n_envs": 4, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.10.11", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.0+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}
|
replay.mp4
CHANGED
Binary files a/replay.mp4 and b/replay.mp4 differ
|
|
results.json
CHANGED
@@ -1 +1 @@
|
|
1 |
-
{"mean_reward": -
|
|
|
1 |
+
{"mean_reward": -3.303347212122753, "std_reward": 0.8480653796876714, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-05-07T16:13:14.504775"}
|
vec_normalize.pkl
CHANGED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
size 2387
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:0fb7b8e69292290b0f8b342f7e2f216af2d2b7d897a29f22d438b71d9f7a299f
|
3 |
size 2387
|