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Daniel Bairamian

Doctorat - McGill
Superviseur⋅e principal⋅e
Derek Nowrouzezahrai
Sujets de recherche
Apprentissage par renforcement
Apprentissage profond
Infographie
Modèles génératifs

Publications

Minimax Exploiter: A Data Efficient Approach for Competitive Self-Play
Daniel Bairamian
Philippe Marcotte
Joshua Romoff
Gabriel Robert
Derek Nowrouzezahrai
Recent advances in Competitive Self-Play (CSP) have achieved, or even surpassed, human level performance in complex game environments such a… (voir plus)s Dota 2 and StarCraft II using Distributed Multi-Agent Reinforcement Learning (MARL). One core component of these methods relies on creating a pool of learning agents -- consisting of the Main Agent, past versions of this agent, and Exploiter Agents -- where Exploiter Agents learn counter-strategies to the Main Agents. A key drawback of these approaches is the large computational cost and physical time that is required to train the system, making them impractical to deploy in highly iterative real-life settings such as video game productions. In this paper, we propose the Minimax Exploiter, a game theoretic approach to exploiting Main Agents that leverages knowledge of its opponents, leading to significant increases in data efficiency. We validate our approach in a diversity of settings, including simple turn based games, the arcade learning environment, and For Honor, a modern video game. The Minimax Exploiter consistently outperforms strong baselines, demonstrating improved stability and data efficiency, leading to a robust CSP-MARL method that is both flexible and easy to deploy.
2023-12-31
AAMAS (publié)
doi.org
arxiv.org