most citedNatural Actor-Critic for Robust Reinforcement Learning with Function Approximation

1 citations · 1 across the 2 of their papers we have counts for

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2 papers

cs.LG2024

Provable Policy Gradient Methods for Average-Reward Markov Potential Games

Min Cheng, Ruida Zhou, P. R. Kumar +1

We study Markov potential games under the infinite horizon average reward criterion. Most previous studies have been for discounted rewards. We prove that both algorithms based on…

cs.LG2023★ 1 cited

Natural Actor-Critic for Robust Reinforcement Learning with Function Approximation

Ruida Zhou, Tao Liu, Min Cheng +3

We study robust reinforcement learning (RL) with the goal of determining a well-performing policy that is robust against model mismatch between the training simulator and the testi…