Minimax Exploiter: A Data Efficient Approach for Competitive Self-Play

• URL: http://arxiv.org/abs/2311.17190v1
• Date: Tue, 28 Nov 2023 19:34:40 GMT
• Title: Minimax Exploiter: A Data Efficient Approach for Competitive Self-Play
• Authors: Daniel Bairamian, Philippe Marcotte, Joshua Romoff, Gabriel Robert, Derek Nowrouzezahrai
• Abstract summary: Minimax Exploiter is a game theoretic approach to exploiting Main Agents that leverages knowledge of its opponents. 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.
• Score: 12.754819077905061
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in Competitive Self-Play (CSP) have achieved, or even surpassed, human level performance in complex game environments such as 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.

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