Variance Reduction based Experience Replay for Policy Optimization
This addresses the challenge of accelerating policy optimization in complex stochastic systems for reinforcement learning practitioners, though it is incremental as it builds on existing experience replay methods.
The paper tackles the problem of inefficient uniform reuse of historical samples in reinforcement learning by proposing a variance reduction based experience replay framework that selectively reuses relevant samples to improve policy gradient estimation, showing it accelerates learning and enhances performance of state-of-the-art methods.
For reinforcement learning on complex stochastic systems where many factors dynamically impact the output trajectories, it is desirable to effectively leverage the information from historical samples collected in previous iterations to accelerate policy optimization. Classical experience replay allows agents to remember by reusing historical observations. However, the uniform reuse strategy that treats all observations equally overlooks the relative importance of different samples. To overcome this limitation, we propose a general variance reduction based experience replay (VRER) framework that can selectively reuse the most relevant samples to improve policy gradient estimation. This selective mechanism can adaptively put more weight on past samples that are more likely to be generated by the current target distribution. Our theoretical and empirical studies show that the proposed VRER can accelerate the learning of optimal policy and enhance the performance of state-of-the-art policy optimization approaches.