Acting upon Imagination: when to trust imagined trajectories in model based reinforcement learning

Integrated architectures for learning, planning, and reacting based on approximating dynamic programming

Richard S. Sutton · 1990

Earlier work this paper cites.

The three sigma rule

Friedrich Pukelsheim · 1994

Earlier work this paper cites.

Introduction to Reinforcement Learning

Richard S. Sutton and Andrew G. Barto · 1998

Earlier work this paper cites.

Model Predictive Control

E.F. Camacho, C. Bordons, and C.B. Alba · 2004

Earlier work this paper cites.

Improving robot dual-system motor learning with intrinsically motivated meta-control and latent-space experience imagination

Original

Muhammad Burhan Hafez, Cornelius Weber, Matthias Kerzel, and Stefan Wermter · 2004

Earlier work this paper cites.

A survey of numerical methods for optimal control

Anvil V. Rao · 2010

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.

Chapter 3 - the cross-entropy method for optimization

Zdravko I. Botev, Dirk P. Kroese, Reuven Y. Rubinstein, and Pierre L’Ecuyer · 2013

Earlier work this paper cites.

Marc Peter Deisenroth, Gerhard Neumann, and Jan Peters, 2013

2013

Earlier work this paper cites.

Learning continuous control policies by stochastic value gradients, 2015

Nicolas Heess, Greg Wayne, David Silver, Timothy Lillicrap, Yuval Tassa, and Tom Erez · 2015

Cited alongside, same era.

Continuous control with deep reinforcement learning

Original

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, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis · 2015

Cited alongside, same era.

Simple and scalable predictive uncertainty estimation using deep ensembles, 2016

Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2016

Cited alongside, same era.

Aggressive deep driving: Model predictive control with a cnn cost model

Paul Drews, Brian Goldfain, Grady Williams, and Evangelos A. Theodorou · 2017

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, 2018

Kurtland Chua, Roberto Calandra, Rowan McAllister, and Sergey Levine · 2018

Later among the works it cites.

Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor, 2018

Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018

Later among the works it cites.

Neural network dynamics for model-based deep reinforcement learning with model-free fine-tuning

Anusha Nagabandi, G. Kahn, Ronald S. Fearing, and S. Levine · 2018

Later among the works it cites.

When to trust your model: Model-based policy optimization

Michael Janner, Justin Fu, Marvin Zhang, and S. Levine · 2019

Later among the works it cites.