220 citations · 278 across the 2 of their papers we have counts for
2 papers
cs.LG2019★ 220 cited
QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning
Kyunghwan Son, Daewoo Kim, Wan Ju Kang +2
We explore value-based solutions for multi-agent reinforcement learning (MARL) tasks in the centralized training with decentralized execution (CTDE) regime popularized recently. Ho…
cs.AI2019★ 58 cited
Learning to Schedule Communication in Multi-agent Reinforcement Learning
Daewoo Kim, Sangwoo Moon, David Hostallero +4
Many real-world reinforcement learning tasks require multiple agents to make sequential decisions under the agents' interaction, where well-coordinated actions among the agents are…