Upload PPO LunarLander-v2 trained agent

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 257.44 +/- 18.27
+      value: 265.08 +/- 20.87
       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 0x7fd776e6e200>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fd776e6e290>", "reset_noise": 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It allows to keep variance\n        above zero and prevent it from growing too fast. 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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 0x7fd776e6e200>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fd776e6e290>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fd776e6e320>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fd776e6e3b0>",
-        "_build": "<function ActorCriticPolicy._build at 0x7fd776e6e440>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7fd776e6e4d0>",
-        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fd776e6e560>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fd776e6e5f0>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7fd776e6e680>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fd776e6e710>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fd776e6e7a0>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fd776e6e830>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7f1794ddff40>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f1794de8040>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f1794de80d0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f1794de8160>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f1794de81f0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f1794de8280>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f1794de8310>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f1794de83a0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f1794de8430>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f1794de84c0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f1794de8550>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f1794de85e0>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x7fd776e69180>"
+        "_abc_impl": "<_abc._abc_data object at 0x7f1794de50c0>"
     },
     "verbose": 1,
     "policy_kwargs": {},
@@ -26,12 +26,12 @@
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": 1684767029111851014,
+    "start_time": 1687272279649618296,
     "learning_rate": 0.0003,
     "tensorboard_log": null,
     "_last_obs": {
         ":type:": "<class 'numpy.ndarray'>",
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     },
     "_last_episode_starts": {
         ":type:": "<class 'numpy.ndarray'>",
@@ -45,7 +45,7 @@
     "_stats_window_size": 100,
     "ep_info_buffer": {
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+oid sha256:8be52c201ae8cf2838a28446724c9ffa129520a305b778868275208dacb4b6f0
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ppo-LunarLander-v2/policy.pth

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 version https://git-lfs.github.com/spec/v1
-oid sha256:f20cf56967eed20aecee07b41a2a6641ab37770435d55ff41b372ef65b1759b2
+oid sha256:733400ec94de66c2c68cc2aed0210521304bf9ade677b641db1b920765e4491a
 size 43329

ppo-LunarLander-v2/system_info.txt

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 - OS: Linux-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023
-- Python: 3.10.11
+- Python: 3.10.12
 - Stable-Baselines3: 2.0.0a5
 - PyTorch: 2.0.1+cu118
 - GPU Enabled: True

results.json

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