Abstract:
As a popular game around the world, Snakes has multiple modes with different settings. In this work, we are dedicated to the 3v3 Snakes, which is characterized by a compl...Show MoreMetadata
Abstract:
As a popular game around the world, Snakes has multiple modes with different settings. In this work, we are dedicated to the 3v3 Snakes, which is characterized by a complex mixture of competition and cooperation. To address this mode of Snakes, most existing AI agents adopt rule based methods, which achieve limited performance due to human’s oversight of some special circumstances. Inspired by the superiority of multi-agent reinforcement learning (MARL), we propose a rule-enhanced multi-agent reinforcement learning algorithm and build a 3v3 Snakes AI. Specifically, we introduce the territory matrix which is commonly utilized in rule based methods to the state features and mask the illegal actions through designed rules. The relationships of individual-team and friends-foes are also merged into reward design. Trained with Distributed PPO and self-play on a single GeForce RTX 2080 GPU for twenty-four hours, our AI achieves state-of-the-art performance and beats human players. On JIDI platform, our agent outperforms the other 132 participating agents and ranks the first for more than 20 consecutive days.
Published in: 2022 IEEE Conference on Games (CoG)
Date of Conference: 21-24 August 2022
Date Added to IEEE Xplore: 20 September 2022
ISBN Information: