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Conceptualization and Implementation of a Reinforcement Learning Approach Using a Case-Based Reasoning Agent in a FPS Scenario

Marcel Kolbe; Pascal Reuß; Jakob Michael Schönborn; Klaus-Dieter Althoff
In: Robert Jäschke; Matthias Weidlich (Hrsg.). Lernen, Wissen, Daten, Analysen. GI-Workshop-Tage "Lernen, Wissen, Daten, Analysen" (LWDA-2019), September 30 - October 2, Berlin, Germany, Pages 280-291, CEUR, 2019.


This paper describes an approach that combines case-based reasoning (CBR) and reinforcement learning (RL) in the context of a first person shooter (FPS) game in the game mode deathmatch. Based on an engine written in C#, Unity and a simple rule-based agent, we propose a FPS agent who is using a combination of case-based reasoning and reinforcement learning to improve the overall performance. The reward function is based on learned sequences of performed small plans and considers the current win chance in a given situation. We describe the implementation of the reinforcement algorithm and the performed evaluation using different starting case bases.

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