Sample Efficient Policy Gradient Methods with Recursive Variance Reduction
This work addresses sample efficiency for reinforcement learning practitioners, offering an incremental improvement over existing stochastic variance reduced policy gradient algorithms.
The authors tackled the problem of sample inefficiency in reinforcement learning by proposing SRVR-PG, a policy gradient algorithm that reduces sample complexity to O(1/ε^{3/2}) episodes to find an ε-approximate stationary point, improving over prior methods by a factor of O(1/ε^{1/6}).
Improving the sample efficiency in reinforcement learning has been a long-standing research problem. In this work, we aim to reduce the sample complexity of existing policy gradient methods. We propose a novel policy gradient algorithm called SRVR-PG, which only requires $O(1/ε^{3/2})$ episodes to find an $ε$-approximate stationary point of the nonconcave performance function $J(\boldsymbolθ)$ (i.e., $\boldsymbolθ$ such that $\|\nabla J(\boldsymbolθ)\|_2^2\leqε$). This sample complexity improves the existing result $O(1/ε^{5/3})$ for stochastic variance reduced policy gradient algorithms by a factor of $O(1/ε^{1/6})$. In addition, we also propose a variant of SRVR-PG with parameter exploration, which explores the initial policy parameter from a prior probability distribution. We conduct numerical experiments on classic control problems in reinforcement learning to validate the performance of our proposed algorithms.