Reinforcement Learning
Reviews: Finite-Time Performance Bounds and Adaptive Learning Rate Selection for Two Time-Scale Reinforcement Learning
NeurIPS 2019 Sun Dec 8th through Sat the 14th, 2019 at Vancouver Convention Center "2626" "Finite-Time Performance Bounds and Adaptive Learning Rate Selection for Two Time-Scale Reinforcement Learning" The reviewers unanimously support acceptance. We encourage the authors to strongly consider the suggestions provided by the reviewers for improving a camera ready version.
We thank all the reviewers for their encouraging comments
We thank all the reviewers for their encouraging comments. In both these cases, τ is effectively zero. Liu et al. shows how GTD-class algorithms can be formally derived using a primal-dual saddle point Sutton et al. presents a (single time-scale) variant of linear TD learning, which they call emphatic TD and show that They also provide an asymptotic convergence analysis to the set of local optima. If the paper is accepted, we will work further on improving the clarity of the work.
Is Q-Learning Provably Efficient?
Chi Jin, Zeyuan Allen-Zhu, Sebastien Bubeck, Michael I. Jordan
Neural Information Processing Systems http://nips.cc/
Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck
Maximilian Igl, Kamil Ciosek, Yingzhen Li, Sebastian Tschiatschek, Cheng Zhang, Sam Devlin, Katja Hofmann
Neural Information Processing Systems http://nips.cc/
Characterizing the Exact Behaviors of Temporal Difference Learning Algorithms Using Markov Jump Linear System Theory
Neural Information Processing Systems http://nips.cc/
The Importance of Sampling inMeta-Reinforcement Learning
Bradly Stadie, Ge Yang, Rein Houthooft, Peter Chen, Yan Duan, Yuhuai Wu, Pieter Abbeel, Ilya Sutskever
This sampling process is embodied as the policyπ, which is responsible for outputting an actiona conditioned on past environmental states{s}.
Learning Abstract Options
Matthew Riemer, Miao Liu, Gerald Tesauro
Neural Information Processing Systems http://nips.cc/