Researchers Introduce Enhanced Deep RL Model For Automated Playtesting

The development of a game involves rounds of playtesting to study players' behaviours and experiences before shaping the final product. However, human playtesting tasks are arduous and repetitive, costly, and can significantly slow down the design and development process. Automated playtesting minimises the need for human intervention. Existing Deep Reinforcement Learning (DRL) game-playing agents can predict both game difficulty and player engagement. Now, researchers from Aalto University and Rovio Entertainment have introduced a novel method for automated playtesting by enhancing DRL with Monte Carlo Tree Search (MCTS). "Our focus is on combining DRL and MCTS to predict pass and churn rates as measures of game difficulty and engagement, and to model the relationship between these