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  # **Q-Learning** Agent playing **FrozenLake-v1**
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  This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
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- ## Usage
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- ```python
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- model = load_from_hub(repo_id="SimingSiming/q-FrozenLake-v1-8x8-non_slippery", filename="q-learning.pkl")
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- # Don't forget to check if you need to add additional attributes (is_slippery=False etc)
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- env = gym.make(model["env_id"])
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- evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ```
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  # **Q-Learning** Agent playing **FrozenLake-v1**
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  This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
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+ n_training_episodes = 200000 # Total training episodes
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+ learning_rate = 0.8 # Learning rate
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+ # Evaluation parameters
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+ n_eval_episodes = 100 # Total number of test episodes
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+
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+ # Environment parameters
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+ env_id = "FrozenLake-v1" # Name of the environment
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+ max_steps = 100 # Max steps per episode
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+ gamma = 0.99 # Discounting rate
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+ eval_seed = [] # The evaluation seed of the environment
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+
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+ # Exploration parameters
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+ epsilon = 1.0 # Exploration rate
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+ max_epsilon = 1.0 # Exploration probability at start
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+ min_epsilon = 0.05 # Minimum exploration probability
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+ decay_rate = 0.00005 # Exponential decay rate for exploration prob
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  ```
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