text: 490,80,1129148265184862208,45724845,Swarat Chaudhuri,"New #ICML2019 paper: ""Control Regularization for Reduced Variance Reinforcement Learning"". Moral: by regularizing DeepRL with a symbolic ""control prior"", you can: 1) learn more efficiently with lower-variance gradients; 2) get provably stable policies. <LINK>",https://arxiv.org/abs/1905.05380,"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. ",Control Regularization for Reduced Variance Reinforcement Learning,1,"['New #ICML2019 paper: ""Control Regularization for Reduced Variance Reinforcement Learning"". Moral: by regularizing DeepRL with a symbolic ""control prior"", you can: 1) learn more efficiently with lower-variance gradients; 2) get provably stable policies. <LINK>']",19,05,259