229 citations · 597 across the 19 of their papers we have counts for
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cs.LG2021★ 1 cited
Semi-On-Policy Training for Sample Efficient Multi-Agent Policy Gradients
Bozhidar Vasilev, Tarun Gupta, Bei Peng +1
Policy gradient methods are an attractive approach to multi-agent reinforcement learning problems due to their convergence properties and robustness in partially observable scenari…
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…