35 citations · 62 across the 7 of their papers we have counts for
3 papers · 1 filter
Provably Efficient Generalized Lagrangian Policy Optimization for Safe Multi-Agent Reinforcement Learning
Dongsheng Ding, Xiaohan Wei, Zhuoran Yang +2
We examine online safe multi-agent reinforcement learning using constrained Markov games in which agents compete by maximizing their expected total rewards under a constraint on ex…
Independent Policy Gradient for Large-Scale Markov Potential Games: Sharper Rates, Function Approximation, and Game-Agnostic Convergence
Dongsheng Ding, Chen-Yu Wei, Kaiqing Zhang +1
We examine global non-asymptotic convergence properties of policy gradient methods for multi-agent reinforcement learning (RL) problems in Markov potential games (MPG). To learn a…
Provably Efficient Safe Exploration via Primal-Dual Policy Optimization
Dongsheng Ding, Xiaohan Wei, Zhuoran Yang +2
We study the Safe Reinforcement Learning (SRL) problem using the Constrained Markov Decision Process (CMDP) formulation in which an agent aims to maximize the expected total reward…