Machine Learning Summit: Simulating Teamfight Tactics Using Deep Learning for Fast Reinforcement Learning AI Training

Session Name:: Machine Learning Summit: Simulating Teamfight Tactics Using Deep Learning for Fast Reinforcement Learning AI Training
Overview:: In 2019, Riot Games launched Teamfight Tactics (TFT) on an expedited timeline. Since then, the TFT game team has been heavily focused on launching new features and content to improve the live game experience. In this talk, Ran Cao, Staff Data Scientist at Riot Games, will present a lightweight path their scientists used to make progress building reinforcement learning agents that learned to play TFT. They built their own version of the game outside of the game engine that leveraged a neural network to predict outcomes rather than fully simulating the game. With this version of the simulator, they are able to minimize the amount of resources required from the game team and still be able to train agents to play TFT at a high skill level and provide gameplay insights. In addition, they were able to make changes to the game in the simulator and test hypotheses with AI that would have been extremely costly to test in the real game.

Session Name:: Machine Learning Summit: Simulating Teamfight Tactics Using Deep Learning for Fast Reinforcement Learning AI Training
Speaker(s):: Ran Cao
Company Name(s):: Riot Games
Track / Format:: Machine Learning Summit

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