Integrating Case-Based Reasoning with Reinforcement Learning for Real-Time Strategy Game Micromanagement

This paper describes the conception of a hybrid Reinforcement Learning (RL) and Case-Based Reasoning (CBR) approach to managing combat units in strategy games. Both methods are combined into an AI agent that is evaluated by using the real-time strategy (RTS) computer game StarCraft as a test bed. The eventual aim of this approach is an AI agent that has the same actions and information at its disposal as a human player. As part of an experimental evaluation, the agent is tested in different scenarios using optimized algorithm parameters. The integration of CBR for memory management is shown to improve the speed of convergence to an optimal policy, while also enabling the agent to address a larger variety of problems when compared to simple RL. The agent manages to beat the built-in game AI and also outperforms a simple RL-only agent. An analysis of the evolution of the case-base shows how scenarios and algorithmic parameters influence agent performance and will serve as a foundation for future improvement to the hybrid CBR/RL approach.