Version-1-PPO-LunarLander-v2

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 294.26 +/- 16.40
+      value: 276.99 +/- 85.73
       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 0x7f8067d0a710>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f8067d0a7a0>", "reset_noise": 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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    ",
-        "__init__": "<function ActorCriticPolicy.__init__ at 0x7f8067d0a710>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f8067d0a7a0>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f8067d0a830>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f8067d0a8c0>",
-        "_build": "<function ActorCriticPolicy._build at 0x7f8067d0a950>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7f8067d0a9e0>",
-        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f8067d0aa70>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f8067d0ab00>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7f8067d0ab90>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f8067d0ac20>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f8067d0acb0>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f8067d0ad40>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x79285a9c3640>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x79285a9c36d0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x79285a9c3760>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x79285a9c37f0>",
+        "_build": "<function ActorCriticPolicy._build at 0x79285a9c3880>",
+        "forward": "<function ActorCriticPolicy.forward at 0x79285a9c3910>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x79285a9c39a0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x79285a9c3a30>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x79285a9c3ac0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x79285a9c3b50>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x79285a9c3be0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x79285a9c3c70>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x7f8067d14440>"
+        "_abc_impl": "<_abc._abc_data object at 0x79285ab69f00>"
     },
     "verbose": 1,
     "policy_kwargs": {},
-    "num_timesteps": 20004864,
-    "_total_timesteps": 20000000,
+    "num_timesteps": 30015488,
+    "_total_timesteps": 30000000,
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": 1714715736679900478,
+    "start_time": 1714730613424232739,
     "learning_rate": 0.0003,
     "tensorboard_log": null,
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-    },
+    "_last_obs": null,
     "_last_episode_starts": {
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@@ -41,17 +38,17 @@
     "_episode_num": 0,
     "use_sde": false,
     "sde_sample_freq": -1,
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     "_stats_window_size": 100,
     "ep_info_buffer": {
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ppo-LunarLander-v2/policy.pth

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results.json

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-{"mean_reward": 294.26406040000006, "std_reward": 16.402414883483996, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-05-03T09:29:20.862713"}
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