andrei-saceleanu
commited on
Commit
·
7fa0da0
1
Parent(s):
cc4f847
Commit #2
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: -3.
|
20 |
name: mean_reward
|
21 |
verified: false
|
22 |
---
|
|
|
16 |
type: PandaReachDense-v2
|
17 |
metrics:
|
18 |
- type: mean_reward
|
19 |
+
value: -3.84 +/- 1.30
|
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 sha256:0d723626cdbcc0327e31f472de551c5c95b2d5b3f031beae5f44705637e15ad8
|
3 |
+
size 108024
|
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_data object at
|
10 |
},
|
11 |
"verbose": 1,
|
12 |
"policy_kwargs": {
|
@@ -41,12 +41,12 @@
|
|
41 |
"_np_random": null
|
42 |
},
|
43 |
"n_envs": 4,
|
44 |
-
"num_timesteps":
|
45 |
-
"_total_timesteps":
|
46 |
"_num_timesteps_at_start": 0,
|
47 |
"seed": null,
|
48 |
"action_noise": null,
|
49 |
-
"start_time":
|
50 |
"learning_rate": 0.0007,
|
51 |
"tensorboard_log": null,
|
52 |
"lr_schedule": {
|
@@ -55,10 +55,10 @@
|
|
55 |
},
|
56 |
"_last_obs": {
|
57 |
":type:": "<class 'collections.OrderedDict'>",
|
58 |
-
":serialized:": "
|
59 |
-
"achieved_goal": "[[0.
|
60 |
-
"desired_goal": "[[
|
61 |
-
"observation": "[[
|
62 |
},
|
63 |
"_last_episode_starts": {
|
64 |
":type:": "<class 'numpy.ndarray'>",
|
@@ -66,9 +66,9 @@
|
|
66 |
},
|
67 |
"_last_original_obs": {
|
68 |
":type:": "<class 'collections.OrderedDict'>",
|
69 |
-
":serialized:": "gAWVuwEAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QoljAAAAAAAAAA6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////
|
70 |
"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]]",
|
71 |
-
"desired_goal": "[[ 0.
|
72 |
"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]]"
|
73 |
},
|
74 |
"_episode_num": 0,
|
@@ -77,13 +77,13 @@
|
|
77 |
"_current_progress_remaining": 0.0,
|
78 |
"ep_info_buffer": {
|
79 |
":type:": "<class 'collections.deque'>",
|
80 |
-
":serialized:": "gAWVHRAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////
|
81 |
},
|
82 |
"ep_success_buffer": {
|
83 |
":type:": "<class 'collections.deque'>",
|
84 |
":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
|
85 |
},
|
86 |
-
"_n_updates":
|
87 |
"n_steps": 5,
|
88 |
"gamma": 0.99,
|
89 |
"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 0x7f47cab3aee0>",
|
8 |
"__abstractmethods__": "frozenset()",
|
9 |
+
"_abc_impl": "<_abc_data object at 0x7f47cab36de0>"
|
10 |
},
|
11 |
"verbose": 1,
|
12 |
"policy_kwargs": {
|
|
|
41 |
"_np_random": null
|
42 |
},
|
43 |
"n_envs": 4,
|
44 |
+
"num_timesteps": 2000000,
|
45 |
+
"_total_timesteps": 2000000,
|
46 |
"_num_timesteps_at_start": 0,
|
47 |
"seed": null,
|
48 |
"action_noise": null,
|
49 |
+
"start_time": 1674717320051366011,
|
50 |
"learning_rate": 0.0007,
|
51 |
"tensorboard_log": null,
|
52 |
"lr_schedule": {
|
|
|
55 |
},
|
56 |
"_last_obs": {
|
57 |
":type:": "<class 'collections.OrderedDict'>",
|
58 |
+
":serialized:": "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",
|
59 |
+
"achieved_goal": "[[ 0.39406857 -0.00347208 0.5382697 ]\n [ 0.39406857 -0.00347208 0.5382697 ]\n [ 0.39406857 -0.00347208 0.5382697 ]\n [ 0.39406857 -0.00347208 0.5382697 ]]",
|
60 |
+
"desired_goal": "[[ 1.0622149 -1.2085651 -0.02003473]\n [-0.32278052 -0.87377024 0.05093261]\n [-0.7653122 -0.7542817 -1.6870148 ]\n [ 0.7030843 -0.4530181 -1.4249271 ]]",
|
61 |
+
"observation": "[[ 0.39406857 -0.00347208 0.5382697 0.0031529 -0.0025194 0.00231244]\n [ 0.39406857 -0.00347208 0.5382697 0.0031529 -0.0025194 0.00231244]\n [ 0.39406857 -0.00347208 0.5382697 0.0031529 -0.0025194 0.00231244]\n [ 0.39406857 -0.00347208 0.5382697 0.0031529 -0.0025194 0.00231244]]"
|
62 |
},
|
63 |
"_last_episode_starts": {
|
64 |
":type:": "<class 'numpy.ndarray'>",
|
|
|
66 |
},
|
67 |
"_last_original_obs": {
|
68 |
":type:": "<class 'collections.OrderedDict'>",
|
69 |
+
":serialized:": "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",
|
70 |
"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]]",
|
71 |
+
"desired_goal": "[[ 0.11867508 0.11551959 0.16617358]\n [-0.05260497 0.04491012 0.02873458]\n [ 0.10672292 -0.03381459 0.14598724]\n [-0.06322829 0.06235264 0.28135177]]",
|
72 |
"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]]"
|
73 |
},
|
74 |
"_episode_num": 0,
|
|
|
77 |
"_current_progress_remaining": 0.0,
|
78 |
"ep_info_buffer": {
|
79 |
":type:": "<class 'collections.deque'>",
|
80 |
+
":serialized:": "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"
|
81 |
},
|
82 |
"ep_success_buffer": {
|
83 |
":type:": "<class 'collections.deque'>",
|
84 |
":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
|
85 |
},
|
86 |
+
"_n_updates": 100000,
|
87 |
"n_steps": 5,
|
88 |
"gamma": 0.99,
|
89 |
"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 sha256:078db70ee5e4cb0817b48212a10114d69fbe397a197c0aed7e1208ec651080eb
|
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 sha256:95612228676a53f5e44bb94003a45372dee9ff29cbce654c9d2365e7403a140f
|
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 0x7efb859093a0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7efb85902a80>"}, "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}}, "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, "num_timesteps": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1674486698888701950, "learning_rate": 0.0007, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/RvAGjbi6x4WUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="}, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[0.36883458 0.02555707 0.5574484 ]\n [0.36883458 0.02555707 0.5574484 ]\n [0.36883458 0.02555707 0.5574484 ]\n [0.36883458 0.02555707 0.5574484 ]]", "desired_goal": "[[-1.2539208 -0.49825916 -0.3385421 ]\n [ 0.17266983 -1.517972 1.5342911 ]\n [ 1.6607 -1.691152 1.0530124 ]\n [ 0.5810423 0.23229675 -0.7413946 ]]", "observation": "[[0.36883458 0.02555707 0.5574484 0.00813808 0.00230426 0.00931659]\n [0.36883458 0.02555707 0.5574484 0.00813808 0.00230426 0.00931659]\n [0.36883458 0.02555707 0.5574484 0.00813808 0.00230426 0.00931659]\n [0.36883458 0.02555707 0.5574484 0.00813808 0.00230426 0.00931659]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "gAWVuwEAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QoljAAAAAAAAAA6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksESwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcoljAAAAAAAAAA9A2fPTHs4z3JEQ8+cOksPS+CJLwG34o98eEKvgPPML1M+zQ9p94MPuuDRz3Oth4+lGgOSwRLA4aUaBJ0lFKUjAtvYnNlcnZhdGlvbpRoByiWYAAAAAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAACUaA5LBEsGhpRoEnSUUpR1Lg==", "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": "[[ 0.07766333 0.11129034 0.13971628]\n [ 0.04221481 -0.0100408 0.0678082 ]\n [-0.13562752 -0.04316617 0.04418497]\n [ 0.1375681 0.04870979 0.15499422]]", "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, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 50000, "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, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "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 0x7f47cab3aee0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f47cab36de0>"}, "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}}, "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, "num_timesteps": 2000000, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1674717320051366011, "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": "[[ 0.39406857 -0.00347208 0.5382697 ]\n [ 0.39406857 -0.00347208 0.5382697 ]\n [ 0.39406857 -0.00347208 0.5382697 ]\n [ 0.39406857 -0.00347208 0.5382697 ]]", "desired_goal": "[[ 1.0622149 -1.2085651 -0.02003473]\n [-0.32278052 -0.87377024 0.05093261]\n [-0.7653122 -0.7542817 -1.6870148 ]\n [ 0.7030843 -0.4530181 -1.4249271 ]]", "observation": "[[ 0.39406857 -0.00347208 0.5382697 0.0031529 -0.0025194 0.00231244]\n [ 0.39406857 -0.00347208 0.5382697 0.0031529 -0.0025194 0.00231244]\n [ 0.39406857 -0.00347208 0.5382697 0.0031529 -0.0025194 0.00231244]\n [ 0.39406857 -0.00347208 0.5382697 0.0031529 -0.0025194 0.00231244]]"}, "_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": "[[ 0.11867508 0.11551959 0.16617358]\n [-0.05260497 0.04491012 0.02873458]\n [ 0.10672292 -0.03381459 0.14598724]\n [-0.06322829 0.06235264 0.28135177]]", "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, "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, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "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": -3.
|
|
|
1 |
+
{"mean_reward": -3.8389509710483254, "std_reward": 1.3001262413458174, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-26T08:48:36.045197"}
|
vec_normalize.pkl
CHANGED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
size 3212
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:f92f7a09280b985b65b372bd6e9d07bd4b39be2fe6b7e91f5059dde627ee9f57
|
3 |
size 3212
|