Upload PPO LunarLander-v2 trained agent. New Course RL 2.0

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 278.88 +/- 22.84
+      value: 290.34 +/- 11.76
       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 0x7f6b8fb52d30>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6b8fb52dc0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6b8fb52e50>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f6b8fb52ee0>", "_build": "<function ActorCriticPolicy._build at 0x7f6b8fb52f70>", "forward": "<function ActorCriticPolicy.forward at 0x7f6b8fb57040>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f6b8fb570d0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f6b8fb57160>", "_predict": "<function ActorCriticPolicy._predict at 0x7f6b8fb571f0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f6b8fb57280>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f6b8fb57310>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f6b8fb573a0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f6b8fb4bf90>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 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ppo-LunarLander-v2-remake.zip

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ppo-LunarLander-v2-remake/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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-        "_build": "<function ActorCriticPolicy._build at 0x7f6b8fb52f70>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7f6b8fb57040>",
-        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f6b8fb570d0>",
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-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f6b8fb57280>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f6b8fb57310>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f6b8fb573a0>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7f868080d1f0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f868080d280>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f868080d310>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f868080d3a0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f868080d430>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f868080d4c0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f868080d550>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f868080d5e0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f868080d670>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f868080d700>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f868080d790>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f868080d820>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7f6b8fb4bf90>"
+        "_abc_impl": "<_abc_data object at 0x7f8680805ae0>"
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     "verbose": 1,
     "policy_kwargs": {},
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         "bounded_below": "[False False False False False False False False]",
         "bounded_above": "[False False False False False False False False]",
-        "_np_random": "RandomState(MT19937)"
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     "action_space": {
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-    "n_envs": 1,
-    "num_timesteps": 2000896,
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     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": 1677417154308989056,
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