Reinforcement Learning Agents for Ubisoft's Roller Champions
This work addresses the skepticism surrounding the practicality of RL in modern video game development for game developers, showing it can be a useful tool for AI design.
This paper demonstrates the practical application of Reinforcement Learning (RL) in modern video game development by presenting an RL system for Ubisoft's Roller Champions. The system trains new models in 1-4 days and adapts to various game modes, developing sophisticated co-ordinated strategies.
In recent years, Reinforcement Learning (RL) has seen increasing popularity in research and popular culture. However, skepticism still surrounds the practicality of RL in modern video game development. In this paper, we demonstrate by example that RL can be a great tool for Artificial Intelligence (AI) design in modern, non-trivial video games. We present our RL system for Ubisoft's Roller Champions, a 3v3 Competitive Multiplayer Sports Game played on an oval-shaped skating arena. Our system is designed to keep up with agile, fast-paced development, taking 1--4 days to train a new model following gameplay changes. The AIs are adapted for various game modes, including a 2v2 mode, a Training with Bots mode, in addition to the Classic game mode where they replace players who have disconnected. We observe that the AIs develop sophisticated co-ordinated strategies, and can aid in balancing the game as an added bonus. Please see the accompanying video at https://vimeo.com/466780171 (password: rollerRWRL2020) for examples.