Reinforcement Learning in Keepaway Framework for RoboCup Simulation League

University essay from Mälardalens högskola/Akademin för innovation, design och teknik


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


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