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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    ",
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-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f8c1cb1f790>",
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     "ep_success_buffer": {
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-    "_n_updates": 2340,
+    "_n_updates": 2532,
     "n_steps": 1024,
     "gamma": 0.999,
     "gae_lambda": 0.99,

LLv2__sb3__ppo__ns=1000000/policy.optimizer.pth

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 version https://git-lfs.github.com/spec/v1
-oid sha256:27228f0f3ef141348a8335a041b5b6080b1c7fd1071edf9f95db812a071120d7
-size 87545
+oid sha256:f01eba9e314b6e59622ec96855c35d0632df5e176dc7961d3c72c48049d85978
+size 88057

LLv2__sb3__ppo__ns=1000000/policy.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:4dbe9c1b40167ff0ab1027f69cdbf3afc74b0cfd342f7c375bcc1a08993e3a8b
-size 43073
+oid sha256:b8108e05818c0ba0bce4431a8daaf25cbdb85c8f8657ade5a09bf0111cd66d48
+size 43201

LLv2__sb3__ppo__ns=1000000/system_info.txt

@@ -1,7 +1,7 @@
-OS: Linux-5.15.0-52-generic-x86_64-with-glibc2.29 #58~20.04.1-Ubuntu SMP Thu Oct 13 13:09:46 UTC 2022
+OS: Linux-5.15.0-56-generic-x86_64-with-glibc2.29 #62~20.04.1-Ubuntu SMP Tue Nov 22 21:24:20 UTC 2022
 Python: 3.8.10
 Stable-Baselines3: 1.6.2
 PyTorch: 1.13.0+cu117
-GPU Enabled: False
+GPU Enabled: True
 Numpy: 1.23.5
 Gym: 0.21.0

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 286.16 +/- 18.95
+      value: 280.42 +/- 17.09
       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 0x7f8c1cb1f1f0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f8c1cb1f280>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f8c1cb1f310>", "_build_mlp_extractor": "<function 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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. 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results.json

@@ -1 +1 @@
-{"mean_reward": 286.1597965751242, "std_reward": 18.95215781345087, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-10T00:09:22.461422"}
+{"mean_reward": 280.4181043741951, "std_reward": 17.087135089734026, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-10T00:40:27.788457"}