Initial commit

README.md

@@ -8,16 +8,17 @@ tags:
 model-index:
 - name: A2C
   results:
-  - metrics:
-    - type: mean_reward
-      value: 1772.97 +/- 413.64
-      name: mean_reward
-    task:
+  - task:
       type: reinforcement-learning
       name: reinforcement-learning
     dataset:
       name: AntBulletEnv-v0
       type: AntBulletEnv-v0
+    metrics:
+    - type: mean_reward
+      value: 1587.19 +/- 175.00
+      name: mean_reward
+      verified: false
 ---
 
 # **A2C** Agent playing **AntBulletEnv-v0**

a2c-AntBulletEnv-v0.zip

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+oid sha256:cbc7ef2d9f29cb3494058efef30d04816608e25163cf3df528328774da6ff1b7
+size 129248

a2c-AntBulletEnv-v0/_stable_baselines3_version

@@ -1 +1 @@
-1.6.0
+1.8.0

a2c-AntBulletEnv-v0/data

@@ -1,27 +1,28 @@
 {
     "policy_class": {
         ":type:": "<class 'abc.ABCMeta'>",
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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 0x7f3e7c4a8290>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f3e7c4a8320>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f3e7c4a83b0>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f3e7c4a8440>",
-        "_build": "<function ActorCriticPolicy._build at 0x7f3e7c4a84d0>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7f3e7c4a8560>",
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-        "_predict": "<function ActorCriticPolicy._predict at 0x7f3e7c4a8680>",
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-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f3e7c4a8830>",
+        "__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 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 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 ActorCriticPolicy.__init__ at 0x7f92ce240160>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f92ce2401f0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f92ce240280>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f92ce240310>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f92ce2403a0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f92ce240430>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f92ce2404c0>",
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+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f92ce240670>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f92ce240700>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f92ce240790>",
         "__abstractmethods__": "frozenset()",
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         "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>",
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a2c-AntBulletEnv-v0/policy.optimizer.pth

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a2c-AntBulletEnv-v0/system_info.txt

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-OS: Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Fri Aug 26 08:44:51 UTC 2022
-Python: 3.7.13
-Stable-Baselines3: 1.6.0
-PyTorch: 1.12.1+cu113
-GPU Enabled: True
-Numpy: 1.21.6
-Gym: 0.21.0
+- 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

config.json

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