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Push Q-Learning agent to Hub
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metadata
tags:
  - FrozenLake-v1-4x4-no_slippery
  - q-learning
  - reinforcement-learning
  - custom-implementation
model-index:
  - name: q-FrozenLake-v1-4x4-noSlippery
    results:
      - metrics:
          - type: mean_reward
            value: 1.00 +/- 0.00
            name: mean_reward
        task:
          type: reinforcement-learning
          name: reinforcement-learning
        dataset:
          name: FrozenLake-v1-4x4-no_slippery
          type: FrozenLake-v1-4x4-no_slippery
  # 使用**Q-Learning**智能体来玩**FrozenLake-v1**
  这是一个使用**Q-Learning**训练有素的模型玩**FrozenLake-v1**.
  
  ## 用法
  ```python
  model = load_from_hub(repo_id='sun1638650145/q-FrozenLake-v1-4x4-noSlippery', filename='q-learning.pkl')

  # 不要忘记检查是否需要添加额外的参数(例如is_slippery=False)
  env = gym.make(model['env_id'])

  evaluate_agent(env, model['max_steps'], model['n_eval_episodes'], model['qtable'], model['eval_seed'])
  
  ```