Fetching the paper…
Reading the bibliography…
This paper prescribes a suite of techniques for off-policy Reinforcement Learning (RL) that simplify the training process and reduce the sample complexity.
Issues in using function approximation for reinforcement learning
Sebastian Thrun and A. Schwartz · 1993
Earlier work this paper cites.
Error bounds for approximate value iteration
Rémi Munos · 2005
Earlier work this paper cites.
Fitted Q-iteration in continuous action-space MDPs
András Antos, Csaba Szepesvári, and Rémi Munos · 2008
Earlier work this paper cites.
Learning near-optimal policies with bellman-residual minimization based fitted policy iteration and a single sample path
András Antos, Csaba Szepesvári, and Rémi Munos · 2008
Earlier work this paper cites.
Error propagation for approximate policy and value iteration
Amir-massoud Farahmand, Csaba Szepesvári, and Rémi Munos · 2010
Earlier work this paper cites.
Linear-time estimators for propensity scores
Deepak Agarwal, Lihong Li, and Alexander Smola · 2011
Earlier work this paper cites.
Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
Earlier work this paper cites.
Playing Atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
Earlier work this paper cites.
A probability path
Sidney I Resnick · 2013
Earlier work this paper cites.
Fast, strong and compliant pneumatic actuation for dexterous tendon-driven hands
Vikash Kumar, Zhe Xu, and Emanuel Todorov · 2013
Earlier work this paper cites.
Deterministic policy gradient algorithms
David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, and Martin Riedmiller · 2014
Cited alongside, same era.
Continuous control with deep reinforcement learning
Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2015
Cited alongside, same era.
Doubly robust covariate shift correction
Sashank J. Reddi, Barnabás Póczos, and Alexander J. Smola · 2015
Cited alongside, same era.
Continuous control with deep reinforcement learning
Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2016
Cited alongside, same era.
Deep reinforcement learning with double q-learning
Hado Van Hasselt, Arthur Guez, and David Silver · 2016
Cited alongside, same era.
Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
Later among the works it cites.
Addressing function approximation error in actor-critic methods
Scott Fujimoto, Herke van Hoof, and Dave Meger · 2018
Later among the works it cites.
Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
Later among the works it cites.
Off-policy deep reinforcement learning without exploration
Scott Fujimoto, David Meger, and Doina Precup · 2018
Later among the works it cites.
Reinforcement learning and optimal control
Dimitri P Bertsekas · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
Cited alongside, same era.
Deep reinforcement learning that matters
Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, and David Meger · 2017
Cited alongside, same era.
Learning complex dexterous manipulation with deep reinforcement learning and demonstrations
Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, Giulia Vezzani, John Schulman, Emanuel Todorov, and Sergey Levine · 2017
Cited alongside, same era.
Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards
Mel Vecerik, Todd Hester, Jonathan Scholz, Fumin Wang, Olivier Pietquin, Bilal Piot, Nicolas Heess, Thomas Rothörl, Thomas Lampe, and Martin Riedmiller · 2017
Cited alongside, same era.
Rasool Fakoor, Pratik Chaudhari, and Alexander J Smola · 2019
Later among the works it cites.
Rasool Fakoor, Pratik Chaudhari, Stefano Soatto, and Alexander J Smola · 2019
Later among the works it cites.
Diagnosing bottlenecks in deep q-learning algorithms
Justin Fu, Aviral Kumar, Matthew Soh, and Sergey Levine · 2019
Later among the works it cites.
Striving for simplicity in off-policy deep reinforcement learning
Rishabh Agarwal, Dale Schuurmans, and Mohammad Norouzi · 2019
Later among the works it cites.