Upload PPO LunarLander-v3 trained agent
Browse files- .gitattributes +1 -0
- README.md +28 -0
- config.json +1 -0
- ppo-LunarLander-v3.zip +3 -0
- ppo-LunarLander-v3/_stable_baselines3_version +1 -0
- ppo-LunarLander-v3/data +94 -0
- ppo-LunarLander-v3/policy.optimizer.pth +3 -0
- ppo-LunarLander-v3/policy.pth +3 -0
- ppo-LunarLander-v3/pytorch_variables.pth +3 -0
- ppo-LunarLander-v3/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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- LunarLander-v2
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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: 162.84 +/- 25.78
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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: LunarLander-v2
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type: LunarLander-v2
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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** 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 0x7f0f87f769d0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f0f87f76a60>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f0f87f76af0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f0f87f76b80>", "_build": "<function 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OS: Linux-5.13.0-40-generic-x86_64-with-glibc2.10 #45~20.04.1-Ubuntu SMP Mon Apr 4 09:38:31 UTC 2022
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Python: 3.8.12
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PyTorch: 1.11.0a0+bfe5ad2
|
5 |
+
GPU Enabled: True
|
6 |
+
Numpy: 1.22.0
|
7 |
+
Gym: 0.21.0
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replay.mp4
ADDED
@@ -0,0 +1,3 @@
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|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:5999afe6e9d141ec2d113a9d177d57ed1833bc4b18c3edc9559b6af1e4545bb3
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3 |
+
size 222314
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results.json
ADDED
@@ -0,0 +1 @@
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|
1 |
+
{"mean_reward": 162.84261078314418, "std_reward": 25.78144715118076, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-04T21:24:16.385411"}
|