Momentum-Based Policy Gradient with Second-Order Information
This work addresses variance reduction in policy gradient methods for reinforcement learning, offering a parameter-free algorithm that improves efficiency without requiring importance sampling, though it appears incremental relative to existing variance-reduction techniques.
The paper tackles the problem of high variance in policy gradient methods for reinforcement learning by proposing SHARP, a variance-reduced algorithm that incorporates second-order information and momentum, achieving an ε-approximate first-order stationary point with O(ε^{-3}) trajectories and a batch size of O(1).
Variance-reduced gradient estimators for policy gradient methods have been one of the main focus of research in the reinforcement learning in recent years as they allow acceleration of the estimation process. We propose a variance-reduced policy-gradient method, called SHARP, which incorporates second-order information into stochastic gradient descent (SGD) using momentum with a time-varying learning rate. SHARP algorithm is parameter-free, achieving $ε$-approximate first-order stationary point with $O(ε^{-3})$ number of trajectories, while using a batch size of $O(1)$ at each iteration. Unlike most previous work, our proposed algorithm does not require importance sampling which can compromise the advantage of variance reduction process. Moreover, the variance of estimation error decays with the fast rate of $O(1/t^{2/3})$ where $t$ is the number of iterations. Our extensive experimental evaluations show the effectiveness of the proposed algorithm on various control tasks and its advantage over the state of the art in practice.