5 papers
Scalable Policy Optimization for Networked Multi-Agent Reinforcement Learning with Continuous State-Action Spaces
Dongming Wang, Pengcheng Dai, Wenwu Yu +1
We develop the Continuous Distributed Coupled Policy Gradient (CDCPG) algorithm for cooperative reinforcement learning in networked Markov decision processes with continuous state…
Distributed Zeroth-Order Policy Gradient for Networked Multi-agent Reinforcement Learning from Human Feedback
Pengcheng Dai, He Wang, Dongming Wang +2
We study a networked multi-agent reinforcement learning (NMARL) problem with human feedback in an infinite-horizon setting, where agents interact over an underlying network with lo…
Distributed scalable coupled policy algorithm for networked multi-agent reinforcement learning
Pengcheng Dai, Dongming Wang, Wenwu Yu +1
This paper studies networked multi-agent reinforcement learning (NMARL) with interdependent rewards and coupled policies. In this setting, each agent's reward depends on its own st…
Distributed primal-dual algorithm for constrained multi-agent reinforcement learning under coupled policies
Pengcheng Dai, He Wang, Dongming Wang +1
This paper investigates constrained multi-agent reinforcement learning (CMARL) in coupled environments, where agents collaboratively maximize the sum of local objectives while sati…
Distributed Neural Policy Gradient Algorithm for Global Convergence of Networked Multi-Agent Reinforcement Learning
Pengcheng Dai, Yuanqiu Mo, Wenwu Yu +1
This paper studies the networked multi-agent reinforcement learning (NMARL) problem, where the objective of agents is to collaboratively maximize the discounted average cumulative…