# Reinforcement Learning Agents for Ubisoft's Roller Champions

arXiv: https://arxiv.org/abs/2012.06031 · HTML: https://mlsanity.com/paper/2012.06031/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2020.

## Built on by (most cited first, 6 of 6)

- [A Survey on Game Playing Agents and Large Models: Methods, Applications, and Challenges](https://mlsanity.com/paper/2403.10249/) (2024)
- [Technical Challenges of Deploying Reinforcement Learning Agents for Game Testing in AAA Games](https://mlsanity.com/paper/2307.11105/) (2023)
- [A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games](https://mlsanity.com/paper/2509.03682/) (2025)
- [Enhancing the Outcome Reward-based RL Training of MLLMs with Self-Consistency Sampling](https://mlsanity.com/paper/2511.10648/) (2025)
- [The Formalism-Implementation Gap in Reinforcement Learning Research](https://mlsanity.com/paper/2510.16175/) (2025)
- [PlotMap: Automated Layout Design for Building Game Worlds](https://mlsanity.com/paper/2309.15242/) (2023)

## Abstract (arXiv)

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.