Upload PPO LunarLander-v2 trained agent

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 224.53 +/- 69.21
+      value: 269.82 +/- 11.94
       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 0x7f2325f22050>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f2325f220e0>", "reset_noise": 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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 0x7f423287c790>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f423287c820>", "reset_noise": 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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    ",
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-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f2325f220e0>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f2325f22170>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f2325f22200>",
-        "_build": "<function ActorCriticPolicy._build at 0x7f2325f22290>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7f2325f22320>",
-        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f2325f223b0>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f2325f22440>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7f2325f224d0>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f2325f22560>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f2325f225f0>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f2325f22680>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7f423287c790>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f423287c820>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f423287c8b0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f423287c940>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f423287c9d0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f423287ca60>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f423287caf0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f423287cb80>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f423287cc10>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f423287cca0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f423287cd30>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f423287cdc0>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x7f2325f28a80>"
+        "_abc_impl": "<_abc._abc_data object at 0x7f42331969c0>"
     },
     "verbose": 1,
     "policy_kwargs": {},
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     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
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     "learning_rate": 0.0003,
     "tensorboard_log": null,
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         "bounded_below": "[ True  True  True  True  True  True  True  True]",
         "bounded_above": "[ True  True  True  True  True  True  True  True]",
@@ -69,22 +69,22 @@
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         "n": "4",
         "start": "0",
         "_shape": [],
         "dtype": "int64",
         "_np_random": null
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-    "n_envs": 1,
-    "n_steps": 1024,
+    "n_envs": 32,
+    "n_steps": 2048,
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     "ent_coef": 0.01,
     "vf_coef": 0.5,
     "max_grad_norm": 0.5,
-    "batch_size": 64,
-    "n_epochs": 4,
+    "batch_size": 128,
+    "n_epochs": 30,
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ppo-LunarLander-v2/pytorch_variables.pth

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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
+- OS: Linux-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sun Apr 28 14:29:16 UTC 2024
+- Python: 3.10.12
 - Stable-Baselines3: 2.0.0a5
-- PyTorch: 2.0.0+cu118
+- PyTorch: 2.3.0+cu121
 - GPU Enabled: True
-- Numpy: 1.22.4
+- Numpy: 1.25.2
 - Cloudpickle: 2.2.1
 - Gymnasium: 0.28.1
 - OpenAI Gym: 0.25.2

results.json

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