Finished model of unit 1
Browse files- README.md +37 -0
- config.json +1 -0
- ppo-LunarLander-v2.zip +3 -0
- ppo-LunarLander-v2/_stable_baselines3_version +1 -0
- ppo-LunarLander-v2/data +96 -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 -0
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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- 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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metrics:
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- type: mean_reward
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value: 200.88 +/- 51.81
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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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TODO: Add your code
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```python
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from stable_baselines3 import ...
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from huggingface_sb3 import load_from_hub
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...
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```
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config.json
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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 0x7a54e8937ac0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7a54e8937b50>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7a54e8937be0>", 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|
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ppo-LunarLander-v2/policy.optimizer.pth
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|
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size 88490
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ppo-LunarLander-v2/policy.pth
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@@ -0,0 +1,3 @@
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|
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|
1 |
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version https://git-lfs.github.com/spec/v1
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oid sha256:78f9bed5d81a0dbd0e656ced56ecc9eb1043f32a32ac6f388e1949deec7ee371
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size 43762
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ppo-LunarLander-v2/pytorch_variables.pth
ADDED
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|
1 |
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version https://git-lfs.github.com/spec/v1
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oid sha256:0c35cea3b2e60fb5e7e162d3592df775cd400e575a31c72f359fb9e654ab00c5
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size 864
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ppo-LunarLander-v2/system_info.txt
ADDED
@@ -0,0 +1,9 @@
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|
|
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|
1 |
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- OS: Linux-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Thu Jun 27 21:05:47 UTC 2024
|
2 |
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- Python: 3.10.12
|
3 |
+
- Stable-Baselines3: 2.0.0a5
|
4 |
+
- PyTorch: 2.4.0+cu121
|
5 |
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- GPU Enabled: True
|
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- Numpy: 1.26.4
|
7 |
+
- Cloudpickle: 2.2.1
|
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- Gymnasium: 0.28.1
|
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+
- OpenAI Gym: 0.25.2
|
replay.mp4
ADDED
Binary file (160 kB). View file
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results.json
ADDED
@@ -0,0 +1 @@
|
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|
|
1 |
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{"mean_reward": 200.87754890000002, "std_reward": 51.811416181167885, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-08-29T11:04:35.230217"}
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