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Author:Gusmão, António
Title:Reinforcement Learning In Real-Time Strategy Games
Publication type:Master's thesis
Publication year:2011
Pages:132      Language:   eng
Department/School:Tietotekniikan laitos
Main subject:Informaatiotekniikka   (T-61)
Supervisor:Oja, Erkki ; Monteiro, José Carlos
Instructor:Raiko, Tapani
OEVS:
Electronic archive copy is available via Aalto Thesis Database.
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Location:P1 Ark Aalto  7131   | Archive
Keywords:reinforcement learning
real-time strategy
games
artificial intelligence
UCT
planning
continuous reinforcement learning
Abstract (eng): We consider the problem of effective and automated decision-making in modern real-time strategy (RTS) games through the use of reinforcement learning techniques.
RTS games constitute environments with large, high-dimensional and continuous state and action spaces with temporally-extended actions.
For such environments, value functions are represented using function approximators.
Due to approximation errors, temporal-difference methods suffer from stability issues.

This thesis proposes Exlos, a stable, model-based Monte-Carlo method which borrows ideas from several existing algorithms including prioritized sweeping and upper confidence trees (UCT).
Contrary to existing model-based algorithms, Exlos assumes models are imperfect, reducing their influence in the decision-making process.
Experimental results in a testing environment show the superiority of Exlos in large discrete state spaces when compared to traditional reinforcement learning methods such as Q-learning and Sarsa.
Furthermore, Exlos is shown to be effective and efficient when operating over value functions represented by approximators.
Its effectiveness is further improved by including a novel online search procedure in the control policy.
As an additional result, we present an improved version of UCT, denoted UCTO, which is experimentally shown to outperform UCT.
ED:2011-12-14
INSSI record number: 43254
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