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Browse files- .gitattributes +1 -0
- README.md +28 -0
- config.json +1 -0
- ppo-CartPole-v1.zip +3 -0
- ppo-CartPole-v1/_stable_baselines3_version +1 -0
- ppo-CartPole-v1/data +94 -0
- ppo-CartPole-v1/policy.optimizer.pth +3 -0
- ppo-CartPole-v1/policy.pth +3 -0
- ppo-CartPole-v1/pytorch_variables.pth +3 -0
- ppo-CartPole-v1/system_info.txt +7 -0
- replay.mp4 +3 -0
- results.json +1 -0
.gitattributes
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README.md
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---
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library_name: stable-baselines3
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tags:
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- CartPole-v1
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- deep-reinforcement-learning
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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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- metrics:
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- type: mean_reward
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value: 500.00 +/- 0.00
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name: mean_reward
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task:
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type: reinforcement-learning
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name: reinforcement-learning
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dataset:
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name: CartPole-v1
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type: CartPole-v1
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---
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# **PPO** Agent playing **CartPole-v1**
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This is a trained model of a **PPO** agent playing **CartPole-v1** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
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## Usage (with Stable-baselines3)
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TODO: Add your code
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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 0x7fae727629d0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fae72762a60>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fae72762af0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fae72762b80>", "_build": "<function 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|
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ppo-CartPole-v1/policy.optimizer.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:fed77557ff645f23703e4d3cf12315a76da58ad2e95a0360d10c2790e617d980
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size 79773
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ppo-CartPole-v1/policy.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:22a8f49396a4bcb308a9680229a73c42370f19a8b34588fd269fac71218af2e6
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size 40641
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ppo-CartPole-v1/pytorch_variables.pth
ADDED
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version https://git-lfs.github.com/spec/v1
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oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
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ppo-CartPole-v1/system_info.txt
ADDED
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OS: Linux-5.11.0-38-generic-x86_64-with-glibc2.31 #42~20.04.1-Ubuntu SMP Tue Sep 28 20:41:07 UTC 2021
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Python: 3.9.12
|
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Stable-Baselines3: 1.5.0
|
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PyTorch: 1.11.0+cu102
|
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GPU Enabled: True
|
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Numpy: 1.22.3
|
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Gym: 0.21.0
|
replay.mp4
ADDED
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version https://git-lfs.github.com/spec/v1
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oid sha256:618a86520cf22579f4e3aeb5b501147975b15368a6928d8ca2cd5a3010203bce
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size 53854
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results.json
ADDED
@@ -0,0 +1 @@
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|
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{"mean_reward": 500.0, "std_reward": 0.0, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-14T18:37:42.866630"}
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