Online adaptation of computer games agents: A reinforcement learning approach
Gustavo Danzi de Andrade, Hugo Santana, André Freire Furtado, Arga Leitão, Geber Lisboa Ramalho · 2004
Designing the behavior of non-player characters that challenges the human player adequately is both a key feature and a big concern in computer games development. This work presents a reinforcement learning (RL) based technique to build intelligent agents that automatically control the game difficulty level, adapting it to the human player’s skills in order to improve the gameplay. The technique is applied to a fighting game, Knock’em, to provide empirical validation of the approach.