Upload PPO LunarLander-v2 trained agent

.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+replay.mp4 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 251.94 +/- 17.46
+      value: 273.11 +/- 21.48
       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 0x787eee9b35b0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x787eee9b3640>", "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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ppo-LunarLander-v2.zip

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ppo-LunarLander-v2/data

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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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+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x78ce0b97fba0>",
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+        ":serialized:": "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"
     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
@@ -87,13 +87,13 @@
     "n_epochs": 4,
     "clip_range": {
         ":type:": "<class 'function'>",
-        ":serialized:": "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"
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     },
     "clip_range_vf": null,
     "normalize_advantage": true,
     "target_kl": null,
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ppo-LunarLander-v2/policy.pth

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ppo-LunarLander-v2/system_info.txt

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-- OS: Linux-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023
-- Python: 3.10.12
+- OS: Linux-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Thu Jun 27 21:05:47 UTC 2024
+- Python: 3.11.11
 - Stable-Baselines3: 2.0.0a5
-- PyTorch: 2.1.0+cu121
+- PyTorch: 2.5.1+cu124
 - GPU Enabled: True
-- Numpy: 1.25.2
-- Cloudpickle: 2.2.1
+- Numpy: 1.26.4
+- Cloudpickle: 3.1.1
 - Gymnasium: 0.28.1
 - OpenAI Gym: 0.25.2

results.json

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