The policy gradient approach is a flexible and powerful reinforcement learning method particularly for problems with continuous actions such as robot control. A common challenge in this scenario is how to reduce the variance of policy gradient estimates for reliable policy updates. In this paper, we combine the following three ideas and give a highly effective policy gradient method: (a) the policy gradients with parameter based exploration, which is a recently proposed policy search method with low variance of gradient estimates, (b) an importance sampling technique, which allows us to reuse previously gathered data in a consistent way, and (c) an optimal baseline, which minimizes the variance of gradient estimates with their unbiasedness being maintained. For the proposed method, we give theoretical analysis of the variance of gradient estimates and show its usefulness through extensive experiments.

本文提出一种有效的基于参数探索的政策梯度算法，通过结合梯度估计的再利用、重要性采样和优化基准线等三个思想，成功地降低了算法的方差，从而实现计算高效的政策更新。理论分析和实验说明了该方法的实用性。

基于参数探索的策略梯度中的高效样本重用