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

Ran Cao  (Staff Data Scientist, Riot Games)

Pass Type: All Access Pass, Summits Pass

Topic: Programming

Format: Lecture

Vault Recording: TBD

Audience Level: All

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.

Takeaway

Attendees will gain insight into an alternative path for demonstrating progression on complex tasks like reinforcement learning without impacting game teams' roadmaps, as well as examples into new types of visualizations and tools that can be leveraged by design teams.

Intended Audience

This is for AI/Gameplay programmers and game designers who are interested in the application and implementation of reinforcement learning for game AI, as well as ML for complex simulation. Basic understanding of machine learning is recommended.