Recognizing the Enemy: Combining Reinforcement Learning with Strategy Selection Using Case-Based Reasoning

Authors:
Bryan Auslander;Stephen Lee-Urban;Chad Hogg;Héctor Muñoz-Avila
Affiliations:
Dept. of Computer Science & Engineering, Lehigh University, Bethlehem, USA;Dept. of Computer Science & Engineering, Lehigh University, Bethlehem, USA;Dept. of Computer Science & Engineering, Lehigh University, Bethlehem, USA;Dept. of Computer Science & Engineering, Lehigh University, Bethlehem, USA
Venue:
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Year:
2008

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Abstract

This paper presents CBRetaliate, an agent that combines Case-Based Reasoning (CBR) and Reinforcement Learning (RL) algorithms. Unlike most previous work where RL is used to improve accuracy in the action selection process, CBRetaliateuses CBR to allow RL to respond more quickly to changing conditions. CBRetaliatecombines two key features: it uses a time window to compute similarity and stores and reuses complete Q-tables for continuous problem solving. We demonstrate CBRetaliateon a team-based first-person shooter game, where our combined CBR+RL approach adapts quicker to changing tactics by an opponent than standalone RL.