Upload PPO LunarLander-v2 trained agent

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 260.57 +/- 18.40
+      value: 286.09 +/- 23.51
       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 0x7ff0ea516550>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ff0ea5165e0>", "reset_noise": 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ppo-LunarLander-v2.zip

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ppo-LunarLander-v2/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    ",
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-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ff0ea516670>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ff0ea516700>",
-        "_build": "<function ActorCriticPolicy._build at 0x7ff0ea516790>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7ff0ea516820>",
-        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7ff0ea5168b0>",
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-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ff0ea516af0>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ff0ea516b80>",
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+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f8d7d2d7820>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f8d7d2d78b0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f8d7d2d7940>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f8d7d2d79d0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f8d7d2d7a60>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f8d7d2d7af0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f8d7d2d7b80>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f8d7d2d7c10>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f8d7d2d7ca0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f8d7d2d7d30>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f8d7d2d7dc0>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x7ff0ea515ac0>"
+        "_abc_impl": "<_abc._abc_data object at 0x7f8d7d6f79c0>"
     },
     "verbose": 1,
     "policy_kwargs": {},
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     "seed": null,
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     "learning_rate": 0.0003,
     "tensorboard_log": null,
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-- OS: Linux-5.4.0-122-generic-x86_64-with-glibc2.31 # 138~18.04.1-Ubuntu SMP Fri Jun 24 14:14:03 UTC 2022
+- OS: Linux-5.4.0-137-generic-x86_64-with-glibc2.31 # 154-Ubuntu SMP Thu Jan 5 17:03:22 UTC 2023
 - Python: 3.9.16
 - Stable-Baselines3: 1.7.0
 - PyTorch: 1.12.1+cu116
 - GPU Enabled: True
-- Numpy: 1.23.4
+- Numpy: 1.23.2
 - Gym: 0.21.0

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