Trust-based Consensus in Multi-Agent Reinforcement Learning Systems. (arXiv:2205.12880v1 [cs.MA])

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 …

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