commit a2c-AntBulletEnv-v0

README.md

@@ -16,7 +16,7 @@ model-index:
       type: AntBulletEnv-v0
     metrics:
     - type: mean_reward
-      value: 626.41 +/- 151.93
+      value: 1547.79 +/- 402.72
       name: mean_reward
       verified: false
 ---

a2c-AntBulletEnv-v0.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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-size 129448
+oid sha256:b601ecd5fe1e6118be9e67c8ec05a19cd4e37dd5e51e39405ae2dbb576d46583
+size 129165

a2c-AntBulletEnv-v0/_stable_baselines3_version

@@ -1 +1 @@
-1.7.0
+1.8.0

a2c-AntBulletEnv-v0/data

@@ -4,20 +4,20 @@
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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 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    ",
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-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x757b2a0fd3b0>",
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     "policy_kwargs": {
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a2c-AntBulletEnv-v0/system_info.txt

@@ -1,7 +1,7 @@
-- OS: Linux-5.15.90+-x86_64-with-debian-bullseye-sid # 1 SMP Sat Mar 25 11:28:03 UTC 2023
+- OS: Linux-5.15.90+-x86_64-with-debian-bullseye-sid # 1 SMP Thu Apr 6 11:02:12 UTC 2023
 - Python: 3.7.12
-- Stable-Baselines3: 1.7.0
-- PyTorch: 1.13.0
-- GPU Enabled: True
+- Stable-Baselines3: 1.8.0
+- PyTorch: 1.13.0+cpu
+- GPU Enabled: False
 - Numpy: 1.21.6
 - Gym: 0.21.0

config.json

@@ -1 +1 @@
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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 0x757b2a0f7d40>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x757b2a0f7dd0>", "reset_noise": 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