Advances in Simulation-Based Search and Batch Reinforcement Learning

Reinforcement learning (RL) defines a general computational problem where the learner must learn to make good decisions through interactive experience. To be effective in solving this problem, the learner must be able to explore the environment, make accurate predictions about the future, and compute strategic plans. These joint challenges distinguish RL from other machine learning problems. This dissertation considers two sub-topics of RL: Planning and Batch RL. For planning, we contribute two novel techniques to improve the efficiency of Monte Carlo Tree Search (MCTS): 1) Memory-augmented MCTS incorporates a memory structure into MCTS in order to generate an approximate value estimate that combines the estimate of similar states; 2) a new MCTS algorithm that applies maximum entropy policy optimization to general sequential decision-making. For batch RL, we offer three analyses towards a better understanding of the theoretical foundations of batch RL: 1) a minimax and instance-dependent analysis of batch policy optimization algorithms; 2) a characterization of the curse of passive data collection in batch RL; and 3) a theoretical analysis of convergence and generalization properties of value prediction algorithms with overparameterized models.