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.

• Type: Conference
• Year: 2008
• Where: EWCBR
• Authors: Bryan Auslander, Stephen Lee-Urban, Chad Hogg, Héctor Muñoz-Avila
• Keywords: Automated Reasoning, Cbretaliate Uses Cbr, EWCBR 2008, Standalone Rl, Team-based First-person Shooter