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--- |
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tags: |
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- Blackjack-v1 |
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- q-learning |
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- reinforcement-learning |
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- custom-implementation |
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library_name: reinforcement-learning |
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model-index: |
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- name: blackjack-qlearning-agent |
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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: Blackjack-v1 |
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type: Blackjack-v1 |
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metrics: |
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- type: mean_reward |
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value: -0.19 +/- 0.95 |
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name: mean_reward |
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verified: false |
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--- |
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# **Q-Learning** Agent playing **Blackjack-v1** |
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## Training Parameters |
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- **Environment ID**: `Blackjack-v1` |
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- **Training Episodes**: 10000 |
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- **Max Steps per Episode**: 99 |
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- **Learning Rate**: 0.7 |
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- **Gamma (Discount Factor)**: 0.95 |
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## Evaluation Results |
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- **Mean Reward**: -0.19 ± 0.95 |
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- **Evaluation Episodes**: 100 |
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## Usage |
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```python |
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from huggingface_hub import hf_hub_download |
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import pickle |
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import gymnasium as gym |
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import numpy as np |
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# 请将下面的占位符替换为你的实际仓库信息 |
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repo_id = "YOUR_USERNAME/YOUR_REPO_NAME" # 替换为你的仓库 |
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filename = "q-learning.pkl" |
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# 加载模型 |
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model_path = hf_hub_download(repo_id=repo_id, filename=filename) |
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with open(model_path, "rb") as f: |
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model = pickle.load(f) |
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# 重建环境 |
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env = gym.make( |
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model["env_id"], |
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render_mode="rgb_array", |
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**model.get("env_config", {}) |
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) |
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# 使用Q表进行推理 |
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qtable = model["qtable"] |
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# 简单的推理示例 |
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state = env.reset() |
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terminated = False |
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while not terminated: |
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# 状态转换为索引 |
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if isinstance(state, tuple): |
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state_idx = model.get("state_to_index", lambda s: s)(state) |
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else: |
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state_idx = state |
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action = np.argmax(qtable[state_idx]) |
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state, reward, terminated, truncated, _ = env.step(action) |
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