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cs.LG2024
A Sharper Global Convergence Analysis for Average Reward Reinforcement Learning via an Actor-Critic Approach
Swetha Ganesh, Washim Uddin Mondal, Vaneet Aggarwal
This work examines average-reward reinforcement learning with general policy parametrization. Existing state-of-the-art (SOTA) guarantees for this problem are either suboptimal or…
cs.LG2024
Order-Optimal Regret with Novel Policy Gradient Approaches in Infinite-Horizon Average Reward MDPs
Swetha Ganesh, Washim Uddin Mondal, Vaneet Aggarwal
We present two Policy Gradient-based algorithms with general parametrization in the context of infinite-horizon average reward Markov Decision Process (MDP). The first one employs…