Concurrent Learning of Control in Multi-agent Sequential Decision Tasks
[Technical Report, Final Report]
University of Southern Mississippi
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The overall objective of this project was to develop multi-agent reinforcement learning MARL approaches for intelligent agents to autonomously learn distributed control policies in decentralized partially observable Markov decision processes Dec-POMDPs, without prior knowledge of the model parameters.