Upload PPO BipedalWalker-v3 trained agent

BipedalWalker-v3.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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BipedalWalker-v3/data

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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 0x7e38025c36d0>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7e38025c3760>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7e38025c37f0>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7e38025c3880>",
-        "_build": "<function ActorCriticPolicy._build at 0x7e38025c3910>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7e38025c39a0>",
-        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7e38025c3a30>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7e38025c3ac0>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7e38025c3b50>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7e38025c3be0>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7e38025c3c70>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7e38025c3d00>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7e44215bf370>",
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+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7e44215bf490>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7e44215bf520>",
+        "_build": "<function ActorCriticPolicy._build at 0x7e44215bf5b0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7e44215bf640>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7e44215bf6d0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7e44215bf760>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7e44215bf7f0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7e44215bf880>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7e44215bf910>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7e44215bf9a0>",
         "__abstractmethods__": "frozenset()",
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+        "_abc_impl": "<_abc._abc_data object at 0x7e44215b5b00>"
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     "verbose": 1,
     "policy_kwargs": {},
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         "high_repr": "1.0",
         "_np_random": null
     },
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-    "n_steps": 125,
+    "n_envs": 4,
+    "n_steps": 500,
     "gamma": 0.999,
     "gae_lambda": 0.98,
     "ent_coef": 0.01,

BipedalWalker-v3/policy.optimizer.pth

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 version https://git-lfs.github.com/spec/v1
-oid sha256:c4f6b8c4496f15b319f5d8032c7dc09d5d3e147ed67ee7e967c04dd6d7d79077
+oid sha256:f8998e9fabdadcd3d33ac6c6512ba4d97a8ceaffefb9d238575987e6c0b01f74
 size 105121

BipedalWalker-v3/policy.pth

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 version https://git-lfs.github.com/spec/v1
-oid sha256:ef771428c8fcf64799c19d1ba5b0d99913b347b8bf1523714ef4893c89dbcfbb
+oid sha256:6eeca3e9b07760fb4d702699798bdc469b09cc1c147682bf1a8cedcde2695954
 size 52143

README.md

@@ -16,7 +16,7 @@ model-index:
       type: BipedalWalker-v3
     metrics:
     - type: mean_reward
-      value: 229.45 +/- 8.82
+      value: -58.54 +/- 86.20
       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 0x7e38025c36d0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7e38025c3760>", "reset_noise": 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results.json

@@ -1 +1 @@
-{"mean_reward": 229.44968091405917, "std_reward": 8.821606648928825, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-04-26T04:56:26.649234"}
+{"mean_reward": -58.536842882012934, "std_reward": 86.19811885320128, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-04-29T06:26:57.227839"}