Upload PPO LunarLander-v2 2e10 trained agent

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 238.85 +/- 65.94
+      value: 286.26 +/- 20.54
       name: mean_reward
       verified: false
 ---

config.json

@@ -1 +1 @@
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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 0x7fbc9693f790>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fbc9693f820>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fbc9693f8b0>", "_build_mlp_extractor": "<function 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-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fbc9693f940>",
-        "_build": "<function ActorCriticPolicy._build at 0x7fbc9693f9d0>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7fbc9693fa60>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fbc9693faf0>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7fbc9693fb80>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fbc9693fc10>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fbc9693fca0>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fbc9693fd30>",
+        "__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 0x7f23066c1d30>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f23066c1dc0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f23066c1e50>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f23066c1ee0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f23066c1f70>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f23066c3040>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f23066c30d0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f23066c3160>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f23066c31f0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f23066c3280>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f23066c3310>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f23066c33a0>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7fbc969400f0>"
+        "_abc_impl": "<_abc._abc_data object at 0x7f23066c4440>"
     },
     "verbose": 1,
     "policy_kwargs": {},
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"
+        ":serialized:": 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     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
         ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
     },
-    "_n_updates": 3908,
+    "_n_updates": 492,
     "n_steps": 1024,
     "gamma": 0.999,
     "gae_lambda": 0.98,
@@ -86,7 +87,7 @@
     "n_epochs": 4,
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ppo-LunarLander-v2/policy.optimizer.pth

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+size 88057

ppo-LunarLander-v2/policy.pth

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ppo-LunarLander-v2/system_info.txt

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-OS: Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022
-Python: 3.8.16
-Stable-Baselines3: 1.6.2
-PyTorch: 1.13.0+cu116
-GPU Enabled: True
-Numpy: 1.21.6
-Gym: 0.21.0
+- OS: Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.9.16
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu116
+- GPU Enabled: True
+- Numpy: 1.22.4
+- Gym: 0.21.0

results.json

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