Adapt the vacuum world (Chapter agents-chapter) for reinforcement learning by including rewards for squares being clean. Make the world observable by providing suitable percepts. Now experiment with different reinforcement learning agents. Is function approximation necessary for success? What sort of approximator works for this application?
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