Second RL model

LunarLander_v2-PPO.zip

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LunarLander_v2-PPO/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 0x7f564bfdb310>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f564bfdb3a0>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f564bfdb430>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f564bfdb4c0>",
-        "_build": "<function ActorCriticPolicy._build at 0x7f564bfdb550>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7f564bfdb5e0>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f564bfdb670>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7f564bfdb700>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f564bfdb790>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f564bfdb820>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f564bfdb8b0>",
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+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f84226279d0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f8422627a60>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f8422627af0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f8422627b80>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f8422627c10>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f8422627ca0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f8422627d30>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f8422627dc0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f8422627e50>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7f564bfd3ae0>"
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     },
     "verbose": 1,
     "policy_kwargs": {},
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     "learning_rate": 0.0003,
     "tensorboard_log": null,
     "lr_schedule": {
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LunarLander_v2-PPO/policy.pth

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LunarLander_v2-PPO/system_info.txt

@@ -2,6 +2,6 @@ OS: Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022
 Python: 3.8.15
 Stable-Baselines3: 1.6.2
 PyTorch: 1.12.1+cu113
-GPU Enabled: True
+GPU Enabled: False
 Numpy: 1.21.6
 Gym: 0.21.0

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 105.35 +/- 66.06
+      value: 254.92 +/- 20.95
       name: mean_reward
       verified: false
 ---

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 0x7f564bfdb310>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f564bfdb3a0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f564bfdb430>", "_build_mlp_extractor": "<function 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results.json

@@ -1 +1 @@
-{"mean_reward": 105.35131052537062, "std_reward": 66.05886312286175, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-06T20:29:15.031054"}
+{"mean_reward": 254.92429886034785, "std_reward": 20.945575573630375, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-07T11:21:20.862539"}