PAGE-PG: A Simple and Loopless Variance-Reduced Policy Gradient Method with Probabilistic Gradient Estimation
This work addresses sample complexity improvements for reinforcement learning practitioners, but it is incremental as it builds on existing variance-reduction techniques.
The paper tackles the high variance issue in policy gradient methods by proposing PAGE-PG, a loopless variance-reduced method based on probabilistic gradient estimation, achieving an average sample complexity of O(ε^{-3}) to reach an ε-stationary solution, matching competitive counterparts.
Despite their success, policy gradient methods suffer from high variance of the gradient estimate, which can result in unsatisfactory sample complexity. Recently, numerous variance-reduced extensions of policy gradient methods with provably better sample complexity and competitive numerical performance have been proposed. After a compact survey on some of the main variance-reduced REINFORCE-type methods, we propose ProbAbilistic Gradient Estimation for Policy Gradient (PAGE-PG), a novel loopless variance-reduced policy gradient method based on a probabilistic switch between two types of updates. Our method is inspired by the PAGE estimator for supervised learning and leverages importance sampling to obtain an unbiased gradient estimator. We show that PAGE-PG enjoys a $\mathcal{O}\left( ε^{-3} \right)$ average sample complexity to reach an $ε$-stationary solution, which matches the sample complexity of its most competitive counterparts under the same setting. A numerical evaluation confirms the competitive performance of our method on classical control tasks.