Configurable Agent With Reward As Input: A Play-Style Continuum Generation
This addresses the problem for game designers needing to anticipate varied player behaviors, though it is incremental as it builds on existing reinforcement learning methods for game testing.
The paper tackles the challenge of simulating diverse play-styles in video games for automated testing by introducing CARI, a configurable agent that generates a continuum of behaviors through a single training loop, outperforming baseline methods in archetype generation.
Modern video games are becoming richer and more complex in terms of game mechanics. This complexity allows for the emergence of a wide variety of ways to play the game across the players. From the point of view of the game designer, this means that one needs to anticipate a lot of different ways the game could be played. Machine Learning (ML) could help address this issue. More precisely, Reinforcement Learning is a promising answer to the need of automating video game testing. In this paper we present a video game environment which lets us define multiple play-styles. We then introduce CARI: a Configurable Agent with Reward as Input. An agent able to simulate a wide continuum range of play-styles. It is not constrained to extreme archetypal behaviors like current methods using reward shaping. In addition it achieves this through a single training loop, instead of the usual one loop per play-style. We compare this novel training approach with the more classic reward shaping approach and conclude that CARI can also outperform the baseline on archetypes generation. This novel agent could be used to investigate behaviors and balancing during the production of a video game with a realistic amount of training time.