1 citations · 2 across the 3 of their papers we have counts for
3 papers
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★ 1 cited
A Riemannian Primal-dual Algorithm Based on Proximal Operator and its Application in Metric Learning
Shijun Wang, Baocheng Zhu, Lintao Ma +1
In this paper, we consider optimizing a smooth, convex, lower semicontinuous function in Riemannian space with constraints. To solve the problem, we first convert it to a dual prob…
cs.LG2020★ 1 cited
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…