24 citations · 29 across the 8 of their papers we have counts for
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cs.LG2020
Riemannian Proximal Policy Optimization
Shijun Wang, Baocheng Zhu, Chen Li +4
In this paper, We propose a general Riemannian proximal optimization algorithm with guaranteed convergence to solve Markov decision process (MDP) problems. To model policy function…
cs.LG2020
Variational Policy Propagation for Multi-agent Reinforcement Learning
Chao Qu, Hui Li, Chang Liu +6
We propose a \emph{collaborative} multi-agent reinforcement learning algorithm named variational policy propagation (VPP) to learn a \emph{joint} policy through the interactions ov…