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Reinforcement Learning in Keepaway Framework for RoboCup Simulation League
Mälardalen University, School of Innovation, Design and Engineering.
2011 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

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


Place, publisher, year, edition, pages
2011. , 33 p.
Keyword [en]
Reinforcement learning, Sarsa, Keepaway framework, Tile coding
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
URN: urn:nbn:se:mdh:diva-13412OAI: diva2:462781
Available from: 2011-12-08 Created: 2011-12-08 Last updated: 2011-12-08Bibliographically approved

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