NeurIPS 2019
Sun Dec 8th through Sat the 14th, 2019 at Vancouver Convention Center
Paper ID:5403
Title:Regularized Anderson Acceleration for Off-Policy Deep Reinforcement Learning

This work is an interesting contribution to deep RL that considers using Anderson acceleration to improve off-policy TD based algorithms. The approach is supported by some theory as well as experiments on standard benchmark problems. Overall, reviewers like the paper and agree it should be accepted.