A consensus-based distributed algorithm for constrained MARL with separable dynamics achieves linear scalability and bounded constraint violations through state-augmented policies and dual variable agreement.
Accelerated Primal-Dual Policy Optimization for Safe Reinforcement Learning
2 Pith papers cite this work, alongside 43 external citations. Polarity classification is still indexing.
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abstract
Constrained Markov Decision Process (CMDP) is a natural framework for reinforcement learning tasks with safety constraints, where agents learn a policy that maximizes the long-term reward while satisfying the constraints on the long-term cost. A canonical approach for solving CMDPs is the primal-dual method which updates parameters in primal and dual spaces in turn. Existing methods for CMDPs only use on-policy data for dual updates, which results in sample inefficiency and slow convergence. In this paper, we propose a policy search method for CMDPs called Accelerated Primal-Dual Optimization (APDO), which incorporates an off-policy trained dual variable in the dual update procedure while updating the policy in primal space with on-policy likelihood ratio gradient. Experimental results on a simulated robot locomotion task show that APDO achieves better sample efficiency and faster convergence than state-of-the-art approaches for CMDPs.
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Develops infinite-horizon stationary robust mean-field games incorporating distributional uncertainty, proves equilibrium existence via fixed-point on contractive Bellman operator, gives convergent algorithm, and derives finite-population approximation bounds under contractive regime.
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Scalable Constrained Multi-Agent Reinforcement Learning via State Augmentation and Consensus for Separable Dynamics
A consensus-based distributed algorithm for constrained MARL with separable dynamics achieves linear scalability and bounded constraint violations through state-augmented policies and dual variable agreement.
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Stationary Robust Mean-Field Games under Model Mismatches
Develops infinite-horizon stationary robust mean-field games incorporating distributional uncertainty, proves equilibrium existence via fixed-point on contractive Bellman operator, gives convergent algorithm, and derives finite-population approximation bounds under contractive regime.