4 papers · 1 filter
Primal-Only Actor Critic Algorithm for Robust Constrained Average Cost MDPs
Anirudh Satheesh, Sooraj Sathish, Swetha Ganesh +2
In this work, we study the problem of finding robust and safe policies in Robust Constrained Average-Cost Markov Decision Processes (RCMDPs). A key challenge in this setting is the…
Efficient Q-Learning and Actor-Critic Methods for Robust Average-Reward Reinforcement Learning
Yang Xu, Swetha Ganesh, Vaneet Aggarwal
We study model-free methods for distributionally robust infinite-horizon average-reward Markov decision processes (MDPs). We present non-asymptotic convergence analyses of Q-learni…
Regret Analysis of Average-Reward Unichain MDPs via an Actor-Critic Approach
Swetha Ganesh, Vaneet Aggarwal
Actor-Critic methods are widely used for their scalability, yet existing theoretical guarantees for infinite-horizon average-reward Markov Decision Processes (MDPs) often rely on r…
Global Convergence for Average Reward Constrained MDPs with Primal-Dual Actor Critic Algorithm
Yang Xu, Swetha Ganesh, Washim Uddin Mondal +2
This paper investigates infinite-horizon average reward Constrained Markov Decision Processes (CMDPs) with general parametrization. We propose a Primal-Dual Natural Actor-Critic al…