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- 1Approximate Value/Policy Iteration
- 1Error Propagation
- 1Machine Learning
- 1Model Selection
- 1Regularization
- 1Regularized Fitted Q-Iteration
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Fall 2011
This thesis studies the reinforcement learning and planning problems that are modeled by a discounted Markov Decision Process (MDP) with a large state space and finite action space. We follow the value-based approach in which a function approximator is used to estimate the optimal value function....
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