Initial Commit

README.md

@@ -10,7 +10,7 @@ model-index:
   results:
   - metrics:
     - type: mean_reward
-      value: 1172.14 +/- 177.58
+      value: 1535.69 +/- 62.23
       name: mean_reward
     task:
       type: reinforcement-learning

a2c-AntBulletEnv-v0.zip

@@ -1,3 +1,3 @@
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-size 129193
+oid sha256:a6a146971d618a154d7683d2f6b7999b3c52f29040a3dbc70a170264deef25cc
+size 136440

a2c-AntBulletEnv-v0/data

@@ -4,19 +4,19 @@
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         "__module__": "stable_baselines3.common.policies",
         "__doc__": "\n    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\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 sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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: Features extractor to use.\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\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 ActorCriticPolicy.__init__ at 0x7f1fbe329b90>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f1fbe329c20>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f1fbe329cb0>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f1fbe329d40>",
-        "_build": "<function ActorCriticPolicy._build at 0x7f1fbe329dd0>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7f1fbe329e60>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f1fbe329ef0>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7f1fbe329f80>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f1fbe2ae050>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f1fbe2ae0e0>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f1fbe2ae170>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7f0a444094d0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f0a44409560>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f0a444095f0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f0a44409680>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f0a44409710>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f0a444097a0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f0a44409830>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f0a444098c0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f0a44409950>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f0a444099e0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f0a44409a70>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7f1fbe2f3a50>"
+        "_abc_impl": "<_abc_data object at 0x7f0a4445b4e0>"
     },
     "verbose": 1,
     "policy_kwargs": {
@@ -33,7 +33,7 @@
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         "high": "[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf]",
         "bounded_below": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False]",
         "bounded_above": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False]",
-        "_np_random": null
+        "_np_random": "RandomState(MT19937)"
     },
     "action_space": {
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a2c-AntBulletEnv-v0/policy.optimizer.pth

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a2c-AntBulletEnv-v0/policy.pth

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config.json

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If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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: Features extractor to use.\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\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 ActorCriticPolicy.__init__ at 0x7f1fbe329b90>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f1fbe329c20>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f1fbe329cb0>", "_build_mlp_extractor": "<function 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