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Workshop: Workshop on Reinforcement Learning Theory

Finite-Sample Analysis of Off-Policy Natural Actor-Critic With Linear Function Approximation

Zaiwei Chen · sajad khodadadian · Siva Maguluri

Abstract: In this paper, we develop a novel variant of off-policy natural actor-critic algorithm with linear function approximation and we establish a sample complexity of $\mathcal{O}(\epsilon^{-3})$, outperforming all the previously known convergence bounds of such algorithms. In order to overcome the divergence due to deadly triad in off-policy policy evaluation under function approximation, we develop a critic that employs $n$-step TD-learning algorithm with a properly chosen $n$. We derive our sample complexity bounds solely based on the assumption that the behavior policy sufficiently explores all the states and actions, which is a much lighter assumption compared to the related literature.

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