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Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
[article]
2020
arXiv
pre-print
In many real-world settings, a team of agents must coordinate its behaviour while acting in a decentralised fashion. At the same time, it is often possible to train the agents in a centralised fashion where global state information is available and communication constraints are lifted. Learning joint action-values conditioned on extra state information is an attractive way to exploit centralised learning, but the best strategy for then extracting decentralised policies is unclear. Our solution
arXiv:2003.08839v2
fatcat:4zm4rksi7jgm7c2le6vp36jvtu