Upload DQN LunarLander-v2 trained agent
Browse files- README.md +37 -0
- config.json +1 -0
- dqn-LunarLander-v2-seed42-12.zip +3 -0
- dqn-LunarLander-v2-seed42-12/_stable_baselines3_version +1 -0
- dqn-LunarLander-v2-seed42-12/data +117 -0
- dqn-LunarLander-v2-seed42-12/policy.optimizer.pth +3 -0
- dqn-LunarLander-v2-seed42-12/policy.pth +3 -0
- dqn-LunarLander-v2-seed42-12/pytorch_variables.pth +3 -0
- dqn-LunarLander-v2-seed42-12/system_info.txt +7 -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: DQN
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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: 130.61 +/- 103.28
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name: mean_reward
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verified: false
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---
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# **DQN** Agent playing **LunarLander-v2**
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This is a trained model of a **DQN** 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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{"mean_reward": 130.6145597325424, "std_reward": 103.28340986901347, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-12T01:28:45.122624"}
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