50 citations · 163 across the 21 of their papers we have counts for
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cs.MA2019★ 14 cited
Multi-agent Hierarchical Reinforcement Learning with Dynamic Termination
Dongge Han, Wendelin Boehmer, Michael Wooldridge +1
In a multi-agent system, an agent's optimal policy will typically depend on the policies chosen by others. Therefore, a key issue in multi-agent systems research is that of predict…
cs.MA2018★ 50 cited
Multi-Agent Common Knowledge Reinforcement Learning
Christian A. Schroeder de Witt, Jakob N. Foerster, Gregory Farquhar +3
Cooperative multi-agent reinforcement learning often requires decentralised policies, which severely limit the agents' ability to coordinate their behaviour. In this paper, we show…