This talk provides an overview of various reinforcement learning algorithms and how they may help game studios create better games, more efficiently. Besides AI development, the ability to train bots to play games during production opens up promising opportunities for automated testing and design assistance. But applying reinforcement learning to modern games brings up many challenges, illustrated here through several examples, with a focus on recent experiments within Ubisoft games. Whether you want to directly learn from pixels to minimize the integration burden, or entirely rewrite your engine to make it more "reinforcement learning-friendly", this presentation is packed with practical tips to help you reach your goal without (too many) tears.

Takeaway

This talk presents to attendees results and lessons learned from several use cases of reinforcement learning in game development, with in particular applications to automated testing of specific game components, as well as learning various AI behaviors (related to decision making, driving, navigation, animation, etc.).

Intended Audience

This talk is directed towards all game developers curious about the promises of reinforcement learning, and willing to know more about potential benefits and pitfalls. Previous knowledge of reinforcement learning is helpful to follow some technical points and practical recommendations, but is not mandatory for a high-level understanding.