An often neglected issue in multi-agent reinforcement learning (MARL) is the potential presence of unreliable agents in the environment whose deviations from expected behavior can prevent a system from accomplishing its intended tasks. In particular, consensus is a fundamental underpinning problem of cooperative distributed multi-agent systems. Consensus requires different agents, situated in a decentralized communication network, to reach an agreement out of a set of initial proposals that they put forward. Learning-based agents should adopt a protocol that allows them to reach consensus despite having one or more unreliable agents in the system. This paper investigates the problem of unreliable agents in MARL, considering consensus as a case study. Echoing established results in the distributed systems literature, our experiments show that even a moderate fraction of such agents can greatly impact the ability of reaching consensus in a networked environment. We propose Reinforcement Learning-based Trusted Consensus (RLTC), a decentralized trust mechanism, in which agents can independently decide which neighbors to communicate with. We empirically demonstrate that our trust mechanism is able to handle unreliable agents effectively, as evidenced by higher consensus success rates.

翻译：在多智能体强化学习（MARL）中，一个常被忽视的问题是环境中可能存在不可靠的智能体，其行为偏离预期会阻碍系统完成既定任务。特别是，共识问题是协作式分布式多智能体系统的一个基础支撑性问题。共识要求处于去中心化通信网络中的不同智能体，能够从各自提出的初始方案集中达成一致。基于学习的智能体应采用一种协议，使得即使在系统中存在一个或多个不可靠智能体的情况下，仍能达成共识。本文以共识问题为案例，研究了MARL中不可靠智能体的问题。与分布式系统文献中的已有结论相呼应，我们的实验表明，即使此类智能体比例适中，也会显著影响网络环境中达成共识的能力。我们提出了基于强化学习的可信共识（RLTC），这是一种去中心化的信任机制，智能体可独立决定与哪些邻居进行通信。我们通过实验证明，该信任机制能够有效处理不可靠智能体，具体表现为更高的共识成功率。