Upload PPO LunarLander-v2 trained agent
Browse files- README.md +4 -4
- config.json +1 -1
- ppo-LunarLander-v2.zip +3 -0
- ppo-LunarLander-v2/_stable_baselines3_version +1 -0
- ppo-LunarLander-v2/data +99 -0
- ppo-LunarLander-v2/policy.optimizer.pth +3 -0
- ppo-LunarLander-v2/policy.pth +3 -0
- ppo-LunarLander-v2/pytorch_variables.pth +3 -0
- ppo-LunarLander-v2/system_info.txt +9 -0
- replay.mp4 +0 -0
- results.json +1 -1
README.md
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@@ -6,7 +6,7 @@ tags:
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- reinforcement-learning
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- stable-baselines3
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model-index:
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- name: PPO
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results:
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- task:
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type: reinforcement-learning
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type: LunarLander-v2
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metrics:
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- type: mean_reward
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value:
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name: mean_reward
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verified: false
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---
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# **PPO
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This is a trained model of a **PPO
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using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
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## Usage (with Stable-baselines3)
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- reinforcement-learning
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- stable-baselines3
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model-index:
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- name: PPO
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results:
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- task:
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type: reinforcement-learning
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type: LunarLander-v2
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metrics:
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- type: mean_reward
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value: 251.68 +/- 13.13
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name: mean_reward
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verified: false
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---
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# **PPO** Agent playing **LunarLander-v2**
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This is a trained model of a **PPO** agent playing **LunarLander-v2**
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using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
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## Usage (with Stable-baselines3)
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config.json
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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 0x7f7793136040>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f77931360d0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f7793136160>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f77931361f0>", "_build": "<function 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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 0x7f4a6a102290>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f4a6a102320>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f4a6a1023b0>", 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ppo-LunarLander-v2/system_info.txt
ADDED
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- OS: Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
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- Python: 3.10.11
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- Stable-Baselines3: 2.0.0a5
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- PyTorch: 2.0.0+cu118
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- GPU Enabled: True
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- Numpy: 1.22.4
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- Cloudpickle: 2.2.1
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- Gymnasium: 0.28.1
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- OpenAI Gym: 0.25.2
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replay.mp4
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Binary files a/replay.mp4 and b/replay.mp4 differ
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results.json
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{"mean_reward":
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{"mean_reward": 251.67999048807124, "std_reward": 13.126328948096454, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-05-05T23:54:50.883598"}
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