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 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 deal with unreliable agents effectively, as evidenced by higher consensus success rates.

该论文探讨了在多智能体强化学习中存在不可靠智能体的问题以及如何实现让智能体在存在不可靠智能体的情况下达成共识，通过提出基于强化学习的可信共识机制，使智能体能够根据与它们之间的交互经验自主决定与何人通信，并取得了更高的共识成功率。

多智能体强化学习系统中基于信任的共识