Upload updated PPO LunarLander-v2 trained agent

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README.md +1 -1
config.json +1 -1
ppo-LunarLander-v2.zip +2 -2
ppo-LunarLander-v2/data +26 -26
ppo-LunarLander-v2/policy.optimizer.pth +1 -1
ppo-LunarLander-v2/policy.pth +1 -1
ppo-LunarLander-v2/system_info.txt +4 -4
results.json +1 -1

README.md CHANGED Viewed

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 276.55 +/- 24.53
       name: mean_reward
       verified: false
 ---

       type: LunarLander-v2
     metrics:
     - type: mean_reward
+      value: 276.76 +/- 18.21
       name: mean_reward
       verified: false
 ---

config.json CHANGED Viewed

@@ -1 +1 @@

- {"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__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 0x7ea091ce4700>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ea091ce4790>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ea091ce4820>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ea091ce48b0>", "_build": "<function ActorCriticPolicy._build at 0x7ea091ce4940>", "forward": "<function ActorCriticPolicy.forward at 0x7ea091ce49d0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7ea091ce4a60>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ea091ce4af0>", "_predict": "<function ActorCriticPolicy._predict at 0x7ea091ce4b80>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ea091ce4c10>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ea091ce4ca0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ea091ce4d30>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7ea091e81880>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1716375976981785444, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": 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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 0x7f82d03adfc0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f82d03ae050>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f82d03ae0e0>", 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@@ -69,14 +69,14 @@
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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 0x7f82d03adfc0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f82d03ae050>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f82d03ae0e0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f82d03ae170>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f82d03ae200>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f82d03ae290>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f82d03ae320>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f82d03ae3b0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f82d03ae440>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f82d03ae4d0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f82d03ae560>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f82d03ae5f0>",
         "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f82d054ea00>"
     },
     "verbose": 1,
     "policy_kwargs": {},
+    "num_timesteps": 10485760,
+    "_total_timesteps": 10000000,
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
+    "start_time": 1716397405644484232,
     "learning_rate": 0.0003,
     "tensorboard_log": null,
     "_last_obs": {
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 - Stable-Baselines3: 2.0.0a5
 - PyTorch: 2.3.0+cu121
 - GPU Enabled: True
-- Numpy: 1.25.2
-- Cloudpickle: 2.2.1
 - Gymnasium: 0.28.1
-- OpenAI Gym: 0.25.2

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 - Stable-Baselines3: 2.0.0a5
 - PyTorch: 2.3.0+cu121
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