This project implements model-based and model-free reinforcement learning algorithms.
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Value Iteration Agent: It utilizes an MDP and runs value iteration for set iterations before the constructor returns. It implements both asynchronous & prioritized sweeping.
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Q-Learning: A RL agent that learns by trial and error from interactions with the environment through its update(state, action, nextState, reward) method. Approximate Q-learning is also implemented