Heuristics for multiagent reinforcement learning in decentralized decision problems

Abstract

Decentralized partially observable Markov decision processes (Dec-POMDPs) model cooperative multiagent scenarios, providing a powerful general framework for team-based artificial intelligence. While optimal algorithms exist for Dec-POMDPs, theoretical and empirical results demonstrate that they are impractical for many problems of real interest. We examine… (More)
DOI: 10.1109/ADPRL.2014.7010642

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