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Q-value Path Decomposition for Deep Multiagent Reinforcement Learning
[article]
2020
arXiv
pre-print
Recently, deep multiagent reinforcement learning (MARL) has become a highly active research area as many real-world problems can be inherently viewed as multiagent systems. A particularly interesting and widely applicable class of problems is the partially observable cooperative multiagent setting, in which a team of agents learns to coordinate their behaviors conditioning on their private observations and commonly shared global reward signals. One natural solution is to resort to the
arXiv:2002.03950v1
fatcat:f3badwvmvjao5gekvekt54smju