Upload best PPO LunarLander-v2 agent (tuned with Optuna).

PPO-LunarLander-v2.zip

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 version https://git-lfs.github.com/spec/v1
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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 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 0x7f6de1176d40>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6de1176dd0>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6de1176e60>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f6de1176ef0>",
-        "_build": "<function ActorCriticPolicy._build at 0x7f6de1176f80>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7f6de117e050>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f6de117e0e0>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7f6de117e170>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f6de117e200>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f6de117e290>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f6de117e320>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7f479d60c4d0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f479d60c560>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f479d60c5f0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f479d60c680>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f479d60c710>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f479d60c7a0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f479d60c830>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f479d60c8c0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f479d60c950>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f479d60c9e0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f479d60ca70>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7f6de11c4ab0>"
+        "_abc_impl": "<_abc_data object at 0x7f479d663240>"
     },
     "verbose": 1,
     "policy_kwargs": {},
@@ -42,12 +42,12 @@
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     "n_envs": 16,
-    "num_timesteps": 1523712,
-    "_total_timesteps": 1512260,
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+    "_total_timesteps": 1462395,
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": 1657112648.298226,
+    "start_time": 1657137853.4587035,
     "learning_rate": 0.0003,
     "tensorboard_log": null,
     "lr_schedule": {
@@ -56,7 +56,7 @@
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PPO-LunarLander-v2/policy.pth

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README.md

@@ -10,7 +10,7 @@ model-index:
   results:
   - metrics:
     - type: mean_reward
-      value: 274.08 +/- 13.94
+      value: 287.70 +/- 14.01
       name: mean_reward
     task:
       type: reinforcement-learning

config.json

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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 0x7f6de1176d40>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6de1176dd0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6de1176e60>", "_build_mlp_extractor": "<function 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results.json

@@ -1 +1 @@
-{"mean_reward": 274.08169726610424, "std_reward": 13.936355892755229, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-07-06T13:32:22.802680"}
+{"mean_reward": 287.7002629210276, "std_reward": 14.01248762767998, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-07-06T20:33:32.219174"}