|Machine Learning Summit: Successfully Use Deep Reinforcement Learning in Testing and NPC Development
|Robin Lindh Nilsson, Jeffrey Shih, Ervin Teng
|Carry Castle, Unity Technologies, Unity Technologies
|Track / Format:
|Machine Learning Summit
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|There have been a lot of discussions (and promise) on how deep reinforcement and imitation learning can be used for scaling playtesting and NPC creation in games. Innovative AI institutions like DeepMind and OpenAI have beaten top players in Go, Starcraft II, and Dota 2, which has led to a flurry of publication and novel research that is making its way to the gaming industry. However, for many game studios today, the hope and dream of creating a truly intelligent AI is in sharp contrast with the cost and implementation realities.nnIn this presentation, you will hear lessons learned from small and indie studios who are leveraging deep reinforcement and imitation learning in order to gain an advantage. You will also hear what advantages these studios are achieving and learn best practices.nnYou will hear real-world examples from two studios who are implementing reinforcement and imitation learning for their titles.