Fetching the paper…
Reading the bibliography…
Model-based reinforcement learning has the potential to be more sample efficient than model-free approaches.
Portfolio selection
Harry Markowitz · 1952
Earlier work this paper cites.
Dyna, an integrated architecture for learning, planning, and reacting
Richard S Sutton · 1991
Earlier work this paper cites.
Policy gradient methods for reinforcement learning with function approximation
Richard S Sutton, David A McAllester, Satinder P Singh, and Yishay Mansour · 2000
Earlier work this paper cites.
Lecture on the calculus of variations and optimal control theory , volume 304
Laurence Chisholm Young · 2000
Earlier work this paper cites.
Td algorithm for the variance of return and mean-variance reinforcement learning
Makoto Sato, Hajime Kimura, and Shibenobu Kobayashi · 2001
Earlier work this paper cites.
Online bagging and boosting
Nikunj C Oza · 2005
Earlier work this paper cites.
An application of reinforcement learning to aerobatic helicopter flight
Pieter Abbeel, Adam Coates, Morgan Quigley, and Andrew Y Ng · 2007
Earlier work this paper cites.
Sampling streaming data with replacement
Byung-Hoon Park, George Ostrouchov, and Nagiza F Samatova · 2007
Earlier work this paper cites.
Pilco: A model-based and data-efficient approach to policy search
Marc Deisenroth and Carl E Rasmussen · 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.
Guided policy search
Sergey Levine and Vladlen Koltun · 2013
Earlier work this paper cites.
Efficient online bootstrapping for large scale learning
Zhen Qin, Vaclav Petricek, Nikos Karampatziakis, Lihong Li, and John Langford · 2013
Cited alongside, same era.
A comprehensive survey on safe reinforcement learning
Javier Garcıa and Fernando Fernández · 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 · 2015
Cited alongside, same era.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
Cited alongside, same era.
Taking the human out of the loop: A review of bayesian optimization
Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P Adams, and Nando De Freitas · 2015
Cited alongside, same era.
Data-efficient reinforcement learning with probabilistic model predictive control
Sanket Kamthe and Marc Peter Deisenroth · 2017
Later among the works it cites.
Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
Later among the works it cites.
Neural network dynamics for model-based deep reinforcement learning with model-free fine-tuning
Anusha Nagabandi, Gregory Kahn, Ronald S. Fearing, and Sergey Levine · 2017
Later among the works it cites.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Later among the works it cites.
Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al · 2017
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.
Learning and policy search in stochastic dynamical systems with bayesian neural networks
Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, and Steffen Udluft · 2016
Cited alongside, same era.
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
Cited alongside, same era.
Improving pilco with bayesian neural network dynamics models
Yarin Gal, Rowan McAllister, and Carl Edward Rasmussen · 2016
Cited alongside, same era.
Deep exploration via bootstrapped dqn
Ian Osband, Charles Blundell, Alexander Pritzel, and Benjamin Van Roy · 2016
Cited alongside, same era.
Later among the works it cites.
Sample-efficient reinforcement learning with stochastic ensemble value expansion
Jacob Buckman, Danijar Hafner, George Tucker, Eugene Brevdo, and Honglak Lee · 2018
Later among the works it cites.
Deep reinforcement learning in a handful of trials using probabilistic dynamics models
Kurtland Chua, Roberto Calandra, Rowan McAllister, and Sergey Levine · 2018
Later among the works it cites.
Model-based reinforcement learning via meta-policy optimization
Ignasi Clavera, Jonas Rothfuss, John Schulman, Yasuhiro Fujita, Tamim Asfour, and Pieter Abbeel · 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.
Model-ensemble trust-region policy optimization
Thanard Kurutach, Ignasi Clavera, Yan Duan, Aviv Tamar, and Pieter Abbeel · 2018
Later among the works it cites.