Resilient Multiagent Reinforcement Learning With Function Approximation

• 成果类型: Article
• 署名作者: Ye, Lintao; Figura, Martin; Lin, Yixuan; Pal, Mainak; Das, Pranoy; Liu, Ji; Gupta, Vijay
• 署名单位: Huazhong University of Science & Technology; Huazhong University of Science & Technology; State University of New York (SUNY) System; Stony Brook University; Purdue University System; Purdue University; State University of New York (SUNY) System; Stony Brook University
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2024.3409676
• 发表日期: 2024
• 页码: 8497-8512
• 关键词: approximation algorithms; vectors; linear programming; Function approximation; training; CONVERGENCE; resilience; Adversarial attacks; Byzantine-resilient learning; consensus; cooperative multiagent reinforcement learning (MARL)

摘要

Adversarial attacks during training can strongly influence the performance of multiagent reinforcement learning algorithms. It is, thus, highly desirable to augment existing algorithms such that the impact of adversarial attacks on cooperative networks is at least bounded. We consider a fully decentralized network, where each agent receives a local reward and observes the global state and action. We propose a resilient consensus-based actor-critic algorithm, whereby each agent estimates the team-average reward and value function, and communicates the associated parameter vectors to its immediate neighbors. We show that in the presence of Byzantine agents, whose estimation and communication strategies are completely arbitrary, the estimates of the cooperative agents converge to a bounded consensus value with probability one, provided that there are at most H Byzantine agents in the network that is (2H+1)-robust. Furthermore, we prove that the policy of the cooperative agents converges with probability one to a bounded neighborhood around a stationary point of their team-average objective function under the assumption that the policies of the adversarial agents asymptotically become stationary.