Cooperative AI for Overcooked

Multi-Agent RL with Population-Based Training

Bachelor thesis (2023)

Authors

I.N. Nestorov Electrical Engineering, Mathematics and Computer Science

Contributors

R.T. Loftin Interactive Intelligence - EEMCS (supervisor 1)
F.A. Oliehoek Interactive Intelligence - EEMCS (supervisor 1)
K.A. Hildebrandt Computer Graphics and Visualisation - EEMCS (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2023 Ivan Nestorov
Artifical Intelligence Collaborative AI Games
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Published Date

28-06-2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

In ad-hoc cooperative environments, the usage of artificial intelligence to take supportive roles and work in collaboration with humans has proven to be of great benefit. The objective of this research is to evaluate the use of population-based training for reinforcement learning agents in a simplified version of the multiplayer game - Overcooked. The method used to answer that question involves evaluating the performance of the agents when paired with a human proxy and their learning curves on different layouts. Based on the employed method, it was concluded that both PBT and other self-play agents display notable underperformance when compared to human proxies and agents trained using human data. Moreover, while the inclusion of mutated agents enhanced sample efficiency in layouts with minimal collision risks, its effect on the final performance of PBT in those layouts was negligible. However, this approach managed to improve performance in layouts where collisions were the primary limiting factor.