協調と競争：進化的マルチエージェント強化学習による群れ形成
Cooperation and Competition: Flocking with Evolutionary Multi-Agent Reinforcement Learning
群れ形成タスクにおいて、boidsモデルに基づく協調報酬と、適応度に応じたパラメータ継承・進化的選択による競争を組み合わせたEMARLを提案し、従来の協調のみ・競争のみの手法を上回ることを示した。
著者: Yunxiao Guo, Xinjia Xie, Runhao Zhao, Chenglan Zhu, Jiangting Yin, Han Long
分類: cs.MA, cs.AI, cs.RO
原文アブストラクト
Flocking is a very challenging problem in a multi-agent system; traditional flocking methods also require complete knowledge of the environment and a precise model for control. In this paper, we propose Evolutionary Multi-Agent Reinforcement Learning (EMARL) in flocking tasks, a hybrid algorithm that combines cooperation and competition with little prior knowledge. As for cooperation, we design the agents' reward for flocking tasks according to the boids model. While for competition, agents with high fitness are designed as senior agents, and those with low fitness are designed as junior, letting junior agents inherit the parameters of senior agents stochastically. To intensify competition, we also design an evolutionary selection mechanism that shows effectiveness on credit assignment in flocking tasks. Experimental results in a range of challenging and self-contrast benchmarks demonstrate that EMARL significantly outperforms the full competition or cooperation methods.