  This paper proposes an exploration technique for multi-agent reinforcement
learning (MARL) with graph-based communication among agents. We assume the
individual rewards received by the agents are independent of the actions by the
other agents, while their policies are coupled. In the proposed framework,
neighbouring agents collaborate to estimate the uncertainty about the
state-action space in order to execute more efficient explorative behaviour.
Different from existing works, the proposed algorithm does not require counting
mechanisms and can be applied to continuous-state environments without
requiring complex conversion techniques. Moreover, the proposed scheme allows
agents to communicate in a fully decentralized manner with minimal information
exchange. And for continuous-state scenarios, each agent needs to exchange only
a single parameter vector. The performance of the algorithm is verified with
theoretical results for discrete-state scenarios and with experiments for
continuous ones.
