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EWCBR
2008
Springer

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

9 years 3 months ago
Recognizing the Enemy: Combining Reinforcement Learning with Strategy Selection Using Case-Based Reasoning
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, CBRetaliate uses CBR to allow RL to respond more quickly to changing conditions. CBRetaliate combines two key features: it uses a time window to compute similarity and stores and reuses complete Q-tables for continuous problem solving. We demonstrate CBRetaliate on a team-based first-person shooter game, where our combined CBR+RL approach adapts quicker to changing tactics by an opponent than standalone RL.
Bryan Auslander, Stephen Lee-Urban, Chad Hogg, H&e
Added 19 Oct 2010
Updated 19 Oct 2010
Type Conference
Year 2008
Where EWCBR
Authors Bryan Auslander, Stephen Lee-Urban, Chad Hogg, Héctor Muñoz-Avila
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