Dealing with high variance is a significant challenge in model-free reinforcement learning (RL). Existing methods are unreliable, exhibiting high variance in performance from run to run using different initializations/seeds. Focusing on problems arising in continuous control, we propose a functional regularization approach to augmenting model-free RL. In particular, we regularize the behavior of the deep policy to be similar to a policy prior, i.e., we regularize in function space. We show that functional regularization yields a bias-variance trade-off, and propose an adaptive tuning strategy to optimize this trade-off. When the policy prior has control-theoretic stability guarantees, we further show that this regularization approximately preserves those stability guarantees throughout learning. We validate our approach empirically on a range of settings, and demonstrate significantly reduced variance, guaranteed dynamic stability, and more efficient learning than deep RL alone.
@article{arxiv.1905.05380,
title = {Control Regularization for Reduced Variance Reinforcement Learning},
author = {Richard Cheng and Abhinav Verma and Gabor Orosz and Swarat Chaudhuri and Yisong Yue and Joel W. Burdick},
journal= {arXiv preprint arXiv:1905.05380},
year = {2019}
}