论文标题
育碧的滚轮冠军的加强学习代理商
Reinforcement Learning Agents for Ubisoft's Roller Champions
论文作者
论文摘要
近年来，强化学习（RL）在研究和流行文化中的流行程度越来越高。但是，怀疑论仍然围绕着现代视频游戏开发中RL的实用性。在本文中，我们以示例证明RL可以成为现代非平凡视频游戏中人工智能（AI）设计的绝佳工具。我们为Ubisoft的Roller Champions介绍了RL系统，这是一款在椭圆形滑冰竞技场上玩过的3V3竞争性多人运动游戏。我们的系统旨在跟上敏捷，快节奏的开发，需要1--4天的时间才能在游戏玩法变化后训练新的型号。除经典游戏模式外，AIS适用于各种游戏模式，包括2V2模式，具有机器人模式的训练，它们取代了断开连接的玩家。我们观察到AIS制定了复杂的协调策略，并可以帮助将游戏作为额外的奖励进行平衡。请参阅https://vimeo.com/466780171（密码：RollerrWrl2020）的随附视频。
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.
