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README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 250.45 +/- 16.86
+      value: 262.58 +/- 12.35
       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 0x7f64fbfd3e50>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f64fbfd3ee0>", "reset_noise": 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ppo-LunarLander-v2.zip

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ppo-LunarLander-v2/_stable_baselines3_version.txt

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+1.6.1

ppo-LunarLander-v2/data.txt

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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 sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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 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 0x7faf6c2d3940>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7faf6c2d39d0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7faf6c2d3a60>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7faf6c2d3af0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7faf6c2d3b80>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7faf6c2d3c10>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7faf6c2d3ca0>",
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+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7faf6c2d3dc0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7faf6c2d3e50>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7faf6c2d3ee0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7faf6c394780>"
+    },
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+        "dtype": "float32",
+        "_shape": [
+            8
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+        "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
+        "high": "[inf inf inf inf inf inf inf inf]",
+        "bounded_below": "[False False False False False False False False]",
+        "bounded_above": "[False False False False False False False False]",
+        "_np_random": null
+    },
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+}

ppo-LunarLander-v2/policy.optimizer.pth

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-size 87929
+oid sha256:37127f5c1aab51fd878a19f26176955d9bedf7911a3187daa464dd721311e50e
+size 87545

ppo-LunarLander-v2/policy.pth

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+size 43073

ppo-LunarLander-v2/system_info.txt

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-- OS: Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
-- Python: 3.9.16
-- Stable-Baselines3: 1.8.0
-- PyTorch: 2.0.0+cu118
-- GPU Enabled: True
-- Numpy: 1.22.4
-- Gym: 0.21.0
+OS: Linux-5.10.16.3-microsoft-standard-WSL2-x86_64-with-glibc2.28 #1 SMP Fri Apr 2 22:23:49 UTC 2021
+Python: 3.9.0
+Stable-Baselines3: 1.6.1
+PyTorch: 1.12.1+cu102
+GPU Enabled: False
+Numpy: 1.23.3
+Gym: 0.21.0

results.json

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-{"mean_reward": 250.44994483714814, "std_reward": 16.861105999210544, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-21T09:08:29.191789"}
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