Upload PPO LunarLander-v2 trained agent

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 36.45 +/- 67.34
+      value: 290.05 +/- 34.60
       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 0x7fb901c73420>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fb901c734c0>", "reset_noise": 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It allows to keep variance\n        above zero and prevent it from growing too fast. 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ppo-LunarLander/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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-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fb901c739c0>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fb901c73a60>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fb901c73b00>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7f9753b33420>",
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+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f9753b33560>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f9753b33600>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f9753b336a0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f9753b33740>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f9753b337e0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f9753b33880>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f9753b33920>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f9753b339c0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f9753b33a60>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f9753b33b00>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x7fb901c5f900>"
+        "_abc_impl": "<_abc._abc_data object at 0x7f9753f89cc0>"
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     "verbose": 1,
     "policy_kwargs": {},
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     "action_noise": null,
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     "tensorboard_log": null,
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     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
         ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
     },
-    "_n_updates": 980,
+    "_n_updates": 122100,
     "observation_space": {
         ":type:": "<class 'gymnasium.spaces.box.Box'>",
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@@ -69,14 +69,14 @@
     },
     "action_space": {
         ":type:": "<class 'gymnasium.spaces.discrete.Discrete'>",
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+        ":serialized:": "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",
         "n": "4",
         "start": "0",
         "_shape": [],
         "dtype": "int64",
         "_np_random": "Generator(PCG64)"
     },
-    "n_envs": 4,
+    "n_envs": 16,
     "n_steps": 1024,
     "gamma": 0.999,
     "gae_lambda": 0.98,
@@ -84,7 +84,7 @@
     "vf_coef": 0.5,
     "max_grad_norm": 0.5,
     "batch_size": 1024,
-    "n_epochs": 4,
+    "n_epochs": 100,
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results.json

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