Variance Reduction for Policy-Gradient Methods via Empirical Variance Minimization
This work addresses a key bottleneck in reinforcement learning for practitioners by offering a potentially more effective variance reduction technique, though it appears incremental as it builds on existing control variate methods.
The paper tackles the high variance problem in policy-gradient methods for reinforcement learning by proposing the Empirical Variance (EV) criterion as an alternative to the popular A2C least-squares approach, showing that EV-based methods can achieve stronger variance reduction and sometimes outperform A2C in experiments.
Policy-gradient methods in Reinforcement Learning(RL) are very universal and widely applied in practice but their performance suffers from the high variance of the gradient estimate. Several procedures were proposed to reduce it including actor-critic(AC) and advantage actor-critic(A2C) methods. Recently the approaches have got new perspective due to the introduction of Deep RL: both new control variates(CV) and new sub-sampling procedures became available in the setting of complex models like neural networks. The vital part of CV-based methods is the goal functional for the training of the CV, the most popular one is the least-squares criterion of A2C. Despite its practical success, the criterion is not the only one possible. In this paper we for the first time investigate the performance of the one called Empirical Variance(EV). We observe in the experiments that not only EV-criterion performs not worse than A2C but sometimes can be considerably better. Apart from that, we also prove some theoretical guarantees of the actual variance reduction under very general assumptions and show that A2C least-squares goal functional is an upper bound for EV goal. Our experiments indicate that in terms of variance reduction EV-based methods are much better than A2C and allow stronger variance reduction.