Upload PPO BipedalWalker-v3 trained agent

PPO_model_v3.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:313193e690588c286a80d64432d46e6418a7c10927752d608c91c6c718516f70
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PPO_model_v3/_stable_baselines3_version

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+2.2.1

PPO_model_v3/data

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+{
+    "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 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 0x16bc46200>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x16bc462a0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x16bc46340>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x16bc463e0>",
+        "_build": "<function ActorCriticPolicy._build at 0x16bc46480>",
+        "forward": "<function ActorCriticPolicy.forward at 0x16bc46520>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x16bc465c0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x16bc46660>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x16bc46700>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x16bc467a0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x16bc46840>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x16bc468e0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x16bc3b600>"
+    },
+    "verbose": 1,
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+            "vf": [
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+        "activation_fn": "<class 'torch.nn.modules.activation.ReLU'>"
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+        "__annotations__": "{'observations': <class 'numpy.ndarray'>, 'actions': <class 'numpy.ndarray'>, 'rewards': <class 'numpy.ndarray'>, 'advantages': <class 'numpy.ndarray'>, 'returns': <class 'numpy.ndarray'>, 'episode_starts': <class 'numpy.ndarray'>, 'log_probs': <class 'numpy.ndarray'>, 'values': <class 'numpy.ndarray'>}",
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+    }
+}

PPO_model_v3/policy.optimizer.pth

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PPO_model_v3/policy.pth

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PPO_model_v3/pytorch_variables.pth

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PPO_model_v3/system_info.txt

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+- OS: macOS-13.4.1-arm64-i386-64bit Darwin Kernel Version 22.5.0: Thu Jun  8 22:22:20 PDT 2023; root:xnu-8796.121.3~7/RELEASE_ARM64_T6000
+- Python: 3.11.3
+- Stable-Baselines3: 2.2.1
+- PyTorch: 2.1.2
+- GPU Enabled: False
+- Numpy: 1.26.3
+- Cloudpickle: 3.0.0
+- Gymnasium: 0.29.1

README.md

@@ -16,7 +16,7 @@ model-index:
       type: BipedalWalker-v3
     metrics:
     - type: mean_reward
-      value: 236.44 +/- 51.60
+      value: 272.12 +/- 2.43
       name: mean_reward
       verified: false
 ---

config.json

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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 0x16b138d60>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x16b138e00>", "reset_noise": "<function 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It allows to keep variance\n        above zero and prevent it from growing too fast. 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results.json

@@ -1 +1 @@
-{"mean_reward": 236.43895092228496, "std_reward": 51.596175398049716, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-01-19T16:10:14.374360"}
+{"mean_reward": 272.12255982664703, "std_reward": 2.427095513285669, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-03-14T16:50:13.207159"}