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Finite-Sample Analysis of Off-Policy Natural Actor-Critic Algorithm

sajad khodadadian · Zaiwei Chen · Siva Maguluri

[ Abstract ] [ Livestream: Visit Reinforcement Learning 12 ] [ Paper ]
Wed 21 Jul 6:30 a.m. — 6:35 a.m. PDT

In this paper, we provide finite-sample convergence guarantees for an off-policy variant of the natural actor-critic (NAC) algorithm based on Importance Sampling. In particular, we show that the algorithm converges to a global optimal policy with a sample complexity of $\mathcal{O}(\epsilon^{-3}\log^2(1/\epsilon))$ under an appropriate choice of stepsizes. In order to overcome the issue of large variance due to Importance Sampling, we propose the $Q$-trace algorithm for the critic, which is inspired by the V-trace algorithm (Espeholt et al., 2018). This enables us to explicitly control the bias and variance, and characterize the trade-off between them. As an advantage of off-policy sampling, a major feature of our result is that we do not need any additional assumptions, beyond the ergodicity of the Markov chain induced by the behavior policy.

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