Upload score 300 trained bipedal walker

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
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+*.mp4 filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+library_name: stable-baselines3
+tags:
+- BipedalWalker-v3
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: TD3
+  results:
+  - metrics:
+    - type: mean_reward
+      value: 302.94 +/- 1.82
+      name: mean_reward
+    task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: BipedalWalker-v3
+      type: BipedalWalker-v3
+---
+
+  # **TD3** Agent playing **BipedalWalker-v3**
+  This is a trained model of a **TD3** agent playing **BipedalWalker-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
+  
+  ## Usage (with Stable-baselines3)
+  TODO: Add your code
+  

TD3_BipedalWalker-v3.zip

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TD3_BipedalWalker-v3/_stable_baselines3_version

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+1.5.0

TD3_BipedalWalker-v3/actor.optimizer.pth

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TD3_BipedalWalker-v3/critic.optimizer.pth

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TD3_BipedalWalker-v3/data

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+        "__module__": "stable_baselines3.td3.policies",
+        "__doc__": "\n    Policy class (with both actor and critic) for TD3.\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 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    :param n_critics: Number of critic networks to create.\n    :param share_features_extractor: Whether to share or not the features extractor\n        between the actor and the critic (this saves computation time)\n    ",
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+    "tau": 0.005,
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+        "__doc__": "\n    Replay buffer used in off-policy algorithms like SAC/TD3.\n\n    :param buffer_size: Max number of element in the buffer\n    :param observation_space: Observation space\n    :param action_space: Action space\n    :param device:\n    :param n_envs: Number of parallel environments\n    :param optimize_memory_usage: Enable a memory efficient variant\n        of the replay buffer which reduces by almost a factor two the memory used,\n        at a cost of more complexity.\n        See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195\n        and https://github.com/DLR-RM/stable-baselines3/pull/28#issuecomment-637559274\n    :param handle_timeout_termination: Handle timeout termination (due to timelimit)\n        separately and treat the task as infinite horizon task.\n        https://github.com/DLR-RM/stable-baselines3/issues/284\n    ",
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+        "__abstractmethods__": "frozenset()",
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+    "target_noise_clip": 0.5,
+    "target_policy_noise": 0.2
+}

TD3_BipedalWalker-v3/policy.pth

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TD3_BipedalWalker-v3/pytorch_variables.pth

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TD3_BipedalWalker-v3/system_info.txt

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+OS: Linux-5.17.5-zen1-1-zen-x86_64-with-arch #1 ZEN SMP PREEMPT Wed, 27 Apr 2022 20:56:14 +0000
+Python: 3.7.12
+Stable-Baselines3: 1.5.0
+PyTorch: 1.11.0+cu113
+GPU Enabled: True
+Numpy: 1.21.6
+Gym: 0.21.0

config.json

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replay.mp4

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+oid sha256:d588a39fc09177f78cd65fa81d9f45c6f7ed2e5954f1f4c99b7aa7319778e957
+size 437668

results.json

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+{"mean_reward": 302.9440686507828, "std_reward": 1.8154824087521553, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-07T14:54:12.640858"}