Dongsheng Ding | DeepAI

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Alejandro Ribeiro
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∙ 06/20/2023

Last-Iterate Convergent Policy Gradient Primal-Dual Methods for Constrained MDPs

We study the problem of computing an optimal policy of an infinite-horiz...

0 Dongsheng Ding, et al. ∙

∙ 05/31/2023

Provably Efficient Generalized Lagrangian Policy Optimization for Safe Multi-Agent Reinforcement Learning

We examine online safe multi-agent reinforcement learning using constrai...

0 Dongsheng Ding, et al. ∙

∙ 06/06/2022

Convergence and sample complexity of natural policy gradient primal-dual methods for constrained MDPs

We study sequential decision making problems aimed at maximizing the exp...

0 Dongsheng Ding, et al. ∙

∙ 02/08/2022

Independent Policy Gradient for Large-Scale Markov Potential Games: Sharper Rates, Function Approximation, and Game-Agnostic Convergence

We examine global non-asymptotic convergence properties of policy gradie...

0 Dongsheng Ding, et al. ∙

∙ 03/01/2020

Provably Efficient Safe Exploration via Primal-Dual Policy Optimization

We study the Safe Reinforcement Learning (SRL) problem using the Constra...

0 Dongsheng Ding, et al. ∙

∙ 10/02/2019

Global exponential stability of primal-dual gradient flow dynamics based on the proximal augmented Lagrangian: A Lyapunov-based approach

For a class of nonsmooth composite optimization problems with linear equ...

0 Dongsheng Ding, et al. ∙

∙ 08/07/2019

Fast multi-agent temporal-difference learning via homotopy stochastic primal-dual optimization

We consider a distributed multi-agent policy evaluation problem in reinf...

0 Dongsheng Ding, et al. ∙

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