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Extending Reinforcement Learning to Provide Dynamic Game Balancing
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2005
Year
Artificial IntelligenceGame AiEngineeringGame TheoryEducationReinforcement Learning (Educational Psychology)Intelligent SystemsMulti-agent LearningAnalogue GamesLifelong Reinforcement LearningLearning In GamesReal-time Fighting GameReinforcement Learning (Computer Engineering)Stochastic GameRobot LearningGeneral Game PlayingMechanism DesignHuman LearningDynamic Game BalancingComputer ScienceOpponent ModellingGamesAdaptive AgentGame Confrontation
A recognized major concern for the game developers’ community is to provide mechanisms to dynamically balance the difficulty level of the games in order to keep the user interested in playing. This work presents an innovative use of reinforcement learning to build intelligent agents that adapt their behavior in order to provide dynamic game balancing. The idea is to couple learning with an action selection mechanism which depends on the evaluation of the current user’s skills. To validate our approach, we applied it to a real-time fighting game, obtaining good results, as the adaptive agent is able to quickly play at the same level as opponents with different skills.