most citedAnchor-Changing Regularized Natural Policy Gradient for Multi-Objective Reinforcement Learning

3 citations · 4 across the 2 of their papers we have counts for

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2 papers

cs.LG2022★ 3 cited

Anchor-Changing Regularized Natural Policy Gradient for Multi-Objective Reinforcement Learning

Ruida Zhou, Tao Liu, Dileep Kalathil +2

We study policy optimization for Markov decision processes (MDPs) with multiple reward value functions, which are to be jointly optimized according to given criteria such as propor…

cs.LG2021★ 1 cited

Policy Optimization for Constrained MDPs with Provable Fast Global Convergence

Tao Liu, Ruida Zhou, Dileep Kalathil +2

We address the problem of finding the optimal policy of a constrained Markov decision process (CMDP) using a gradient descent-based algorithm. Previous results have shown that a pr…