Reinforcement Learning Agents for Ubisoft's Roller Champions

• URL: http://arxiv.org/abs/2012.06031v1
• Date: Thu, 10 Dec 2020 23:53:15 GMT
• Title: Reinforcement Learning Agents for Ubisoft's Roller Champions
• Authors: Nancy Iskander, Aurelien Simoni, Eloi Alonso, Maxim Peter
• Abstract summary: 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. We observe that the AIs develop sophisticated co-ordinated strategies, and can aid in balancing the game as an added bonus.
• Score: 0.26249027950824505
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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