Upload PPO LunarLander-v2 trained agent

PPO-relu-2M.zip

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PPO-relu-2M/_stable_baselines3_version

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+2.2.1

PPO-relu-2M/data

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+}

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PPO-relu-2M/pytorch_variables.pth

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PPO-relu-2M/system_info.txt

@@ -0,0 +1,8 @@
+- OS: macOS-10.16-x86_64-i386-64bit Darwin Kernel Version 21.5.0: Tue Apr 26 21:08:37 PDT 2022; root:xnu-8020.121.3~4/RELEASE_ARM64_T6000
+- Python: 3.10.13
+- Stable-Baselines3: 2.2.1
+- PyTorch: 1.13.1
+- GPU Enabled: False
+- Numpy: 1.26.2
+- Cloudpickle: 3.0.0
+- Gymnasium: 0.29.1

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 250.46 +/- 16.42
+      value: 285.86 +/- 17.35
       name: mean_reward
       verified: false
 ---

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 0x7fae5bf19bd0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fae5bf19c60>", "reset_noise": 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0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "rollout_buffer_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVNgAAAAAAAACMIHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5idWZmZXJzlIwNUm9sbG91dEJ1ZmZlcpSTlC4=", "__module__": "stable_baselines3.common.buffers", "__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'>}", "__doc__": "\n    Rollout buffer used in on-policy algorithms like A2C/PPO.\n    It corresponds to ``buffer_size`` transitions collected\n    using the current policy.\n    This experience will be discarded after the policy update.\n    In order to use PPO objective, we also store the current value of each state\n    and the log probability of each taken action.\n\n    The term rollout here refers to the model-free notion and should not\n    be used with the concept of rollout used in model-based RL or planning.\n    Hence, it is only involved in policy and value function training but not action selection.\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: PyTorch device\n    :param gae_lambda: Factor for trade-off of bias vs variance for Generalized Advantage Estimator\n        Equivalent to classic advantage when set to 1.\n    :param gamma: Discount factor\n    :param n_envs: Number of parallel environments\n    ", "__init__": "<function RolloutBuffer.__init__ at 0x7fae5be9f9a0>", "reset": "<function RolloutBuffer.reset at 0x7fae5be9fa30>", "compute_returns_and_advantage": "<function RolloutBuffer.compute_returns_and_advantage at 0x7fae5be9fac0>", "add": "<function RolloutBuffer.add at 0x7fae5be9fb50>", "get": "<function RolloutBuffer.get at 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results.json

@@ -1 +1 @@
-{"mean_reward": 250.46486999999996, "std_reward": 16.42150634865491, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-11-24T22:27:56.293104"}
+{"mean_reward": 285.8610038, "std_reward": 17.354392196612295, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-11-25T22:00:19.862688"}