Reinforcement Learning in Keepaway Framework for RoboCup Simulation League
Abstract: This thesis aims to apply the reinforcement learning into soccer robot and show the great power of reinforcement learning for the RoboCup. In the first part, the background of reinforcement learning is briefly introduced before showing the previous work on it. Therefore the difficulty in implementing reinforcement learning is proposed. The second section demonstrates basic concepts in reinforcement learning, including three fundamental elements, state, action and reward respectively, and three classical approaches, dynamic programming, monte carlo methods and temporal-difference learning respectively. When it comes to keepaway framework, more explanations are given to further combine keepaway with reinforcement learning. After the suggestion about sarsa algorithm with two function approximation, artificial neural network and tile coding, it is implemented successfully during the simulations. The results show it significantly improves the performance of soccer robot.
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