Second try with 2 million time steps

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 241.33 +/- 74.88
+      value: 276.03 +/- 24.34
       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 0x7f624f375ab0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f624f375b40>", "reset_noise": 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         "__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    ",
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-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f624f376050>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f624f3760e0>",
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+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7d88efaa0e50>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7d88efaa0ee0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7d88efaa0f70>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7d88efaa1000>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7d88efaa1090>",
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+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7d88efaa12d0>",
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         "__abstractmethods__": "frozenset()",
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     "tensorboard_log": null,
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     "ep_success_buffer": {
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-    "_n_updates": 3908,
+    "_n_updates": 492,
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         ":type:": "<class 'gymnasium.spaces.box.Box'>",
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         "bounded_below": "[ True  True  True  True  True  True  True  True]",
         "bounded_above": "[ True  True  True  True  True  True  True  True]",
@@ -65,18 +65,18 @@
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         "low_repr": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]",
         "high_repr": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]",
-        "_np_random": "Generator(PCG64)"
+        "_np_random": null
     },
     "action_space": {
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         "n": "4",
         "start": "0",
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     },
-    "n_envs": 1,
+    "n_envs": 16,
     "n_steps": 1024,
     "gamma": 0.999,
     "gae_lambda": 0.98,

ppo-LunarLander-v2/policy.optimizer.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:3a530e8c45c247c9740e8774fa8f83843512fc2a3cb0019895e3b51ec7c545e8
-size 88362
+oid sha256:574e819e17e9ac68e8a0ad8fa6c9f88b1987d5f96f4d59bce08bd575af805343
+size 87978

ppo-LunarLander-v2/policy.pth

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 version https://git-lfs.github.com/spec/v1
-oid sha256:34ae3d17d84e902b2690bbbfaa0f2e526729ce7a39a8164458bfbd4994d79d1b
-size 43762
+oid sha256:1af2956f0143ed41f4fbea1e96f9a12495838bec776cc410f125fcbaf80b6ccc
+size 43634

ppo-LunarLander-v2/system_info.txt

@@ -2,7 +2,7 @@
 - Python: 3.10.12
 - Stable-Baselines3: 2.0.0a5
 - PyTorch: 2.3.0+cu121
-- GPU Enabled: True
+- GPU Enabled: False
 - Numpy: 1.25.2
 - Cloudpickle: 2.2.1
 - Gymnasium: 0.28.1

results.json

@@ -1 +1 @@
-{"mean_reward": 241.33160603130926, "std_reward": 74.88430893965653, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-05-28T13:24:17.634893"}
+{"mean_reward": 276.02775231280026, "std_reward": 24.34037990257214, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-05-29T11:35:33.659737"}