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Decentralized bayesian reinforcement learning for online agent collaboration

Parr G., Farinelli A., Rogers A. , Chalkiadakis Georgios, Jennings N. R., McClean S., Teacy W. T. L.

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Περίληψη

Solving complex but structured problems in a decentralized manner via multiagent collaboration has received much attention in recent years. This is natural, as on one hand, multiagent systems usu- ally possess a structure that determines the allowable interactions among the agents; and on the other hand, the single most pressing need in a cooperative multiagent system is to coordinate the local policies of autonomous agents with restricted capabilities to serve a system-wide goal. The presence of uncertainty makes this even more challenging, as the agents face the additional need to learn the unknown environment parameters while forming (and follow- ing) local policies in an online fashion. In this paper, we provide the first Bayesian reinforcement learning (BRL) approach for dis- tributed coordination and learning in a cooperative multiagent sys- tem by devising two solutions to this type of problem. More specif- ically, we show how the Value of Perfect Information (VPI) can be used to perform efficient decentralised exploration in both model- based and model-free BRL, and in the latter case, provide a closed form solution for VPI, correcting a decade old result by Dearden, Friedman and Russell. To evaluate these solutions, we present ex- perimental results comparing their relative merits, and demonstrate empirically that both solutions outperform an existing multiagent learning method, representative of the state-of-the-art.

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