On the Convergence Rate of Off-Policy Policy Optimization Methods with Density-Ratio Correction

Jiawei Huang; Nan Jiang

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arXiv · 2106.00993

On the Convergence Rate of Off-Policy Policy Optimization Methods with Density-Ratio Correction

Jiawei Huang·Nan Jiang

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Abstract

In this paper, we study the convergence properties of off-policy policy improvement algorithms with state-action density ratio correction under function approximation setting, where the objective function is formulated as a max-max-min optimization problem. We characterize the bias of the learning objective and present two strategies with finite-time convergence guarantees. In our first strategy, we present algorithm P-SREDA with convergence rate $O(\epsilon^{-3})$, whose dependency on $\epsilon$ is optimal. In our second strategy, we propose a new off-policy actor-critic style algorithm named O-SPIM. We prove that O-SPIM converges to a stationary point with total complexity $O(\epsilon^{-4})$, which matches the convergence rate of some recent actor-critic algorithms in the on-policy setting.

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BibTeX RIS

Jiawei Huang, Nan Jiang. 2021-06-02. On the Convergence Rate of Off-Policy Policy Optimization Methods with Density-Ratio Correction. https://arxiv.org/abs/2106.00993

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