16 citations · 62 across the 13 of their papers we have counts for
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cs.LG2021★ 9 cited
Plan Better Amid Conservatism: Offline Multi-Agent Reinforcement Learning with Actor Rectification
Ling Pan, Longbo Huang, Tengyu Ma +1
Conservatism has led to significant progress in offline reinforcement learning (RL) where an agent learns from pre-collected datasets. However, as many real-world scenarios involve…
cs.LG2021★ 16 cited
Regularized Softmax Deep Multi-Agent -Learning
Ling Pan, Tabish Rashid, Bei Peng +2
Tackling overestimation in -learning is an important problem that has been extensively studied in single-agent reinforcement learning, but has received comparatively little atte…