Fetching the paper…
Reading the bibliography…
There is a widespread intuition that model-based control methods should be able to surpass the data efficiency of model-free approaches.
Novel approach to nonlinear/non-gaussian bayesian state estimation
Neil J Gordon, David J Salmond, and Adrian FM Smith · 1993
Earlier work this paper cites.
An application of reinforcement learning to aerobatic helicopter flight
Pieter Abbeel, Adam Coates, Morgan Quigley, and Andrew Y Ng · 2006
Earlier work this paper cites.
A reduction of imitation learning and structured prediction to no-regret online learning
Stéphane Ross, Geoffrey Gordon, and Drew Bagnell · 2011
Earlier work this paper cites.
Synthesis and stabilization of complex behaviors through online trajectory optimization
Yuval Tassa, Tom Erez, and Emanuel Todorov · 2012
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.
Mopo: Model-based offline policy optimization
Tianhe Yu, Garrett Thomas, Lantao Yu, Stefano Ermon, James Y Zou, Sergey Levine, Chelsea Finn, and Tengyu Ma · 2012
Earlier work this paper cites.
The cross-entropy method for optimization
Z I Botev, D P Kroese, R Y Rubinstein, and P L’Ecuyer · 2013
Earlier work this paper cites.
Guided policy search
Sergey Levine and Vladlen Koltun · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Combining the benefits of function approximation and trajectory optimization
Igor Mordatch and Emo Todorov · 2014
Earlier work this paper cites.
Control-limited differential dynamic programming
Yuval Tassa, Nicolas Mansard, and Emo Todorov · 2014
Earlier work this paper cites.
Learning continuous control policies by stochastic value gradients
Nicolas Heess, Greg Wayne, David Silver, Timothy Lillicrap, Yuval Tassa, and Tom Erez · 2015
Earlier work this paper cites.
Interactive control of diverse complex characters with neural networks
Igor Mordatch, Kendall Lowrey, Galen Andrew, Zoran Popovic, and Emanuel V Todorov · 2015
Earlier work this paper cites.
Optimization-based locomotion planning, estimation, and control design for the atlas humanoid robot
Scott Kuindersma, Robin Deits, Maurice Fallon, Andrés Valenzuela, Hongkai Dai, Frank Permenter, Twan Koolen, Pat Marion, and Russ Tedrake · 2016
Earlier work this paper cites.
Safe and efficient off-policy reinforcement learning
Rémi Munos, Tom Stepleton, Anna Harutyunyan, and Marc G Bellemare · 2016
Earlier work this paper cites.
Fast nonlinear model predictive control for unified trajectory optimization and tracking
Michael Neunert, Cedric De Crousaz, Fadri Furrer, Mina Kamel, Farbod Farshidian, Roland Siegwart, and Jonas Buchli · 2016
Earlier work this paper cites.
Emergence of locomotion behaviours in rich environments
Nicolas Heess, Dhruva TB, Srinivasan Sriram, Jay Lemmon, Josh Merel, Greg Wayne, Yuval Tassa, Tom Erez, Ziyu Wang, SM Eslami, et al · 2017
Earlier work this paper cites.
Agile autonomous driving using end-to-end deep imitation learning
Yunpeng Pan, Ching-An Cheng, Kamil Saigol, Keuntaek Lee, Xinyan Yan, Evangelos Theodorou, and Byron Boots · 2017
Earlier work this paper cites.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Earlier work this paper cites.
Model predictive path integral control: From theory to parallel computation
Grady Williams, Andrew Aldrich, and Evangelos A Theodorou · 2017
Cited alongside, same era.
Maximum a posteriori policy optimisation
Abbas Abdolmaleki, Jost Tobias Springenberg, Yuval Tassa, Rémi Munos, Nicolas Heess, and Martin A Riedmiller · 2018
Cited alongside, same era.
Deep reinforcement learning in a handful of trials using probabilistic dynamics models
Kurtland Chua, Roberto Calandra, Rowan McAllister, and Sergey Levine · 2018
Cited alongside, same era.
Learning latent dynamics for planning from pixels
Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, and James Davidson · 2018
Cited alongside, same era.
Deepmimic: Example-guided deep reinforcement learning of physics-based character skills
Xue Bin Peng, Pieter Abbeel, Sergey Levine, and Michiel van de Panne · 2018
Cited alongside, same era.
Comic: Complementary task learning & mimicry for reusable skills
Leonard Hasenclever, Fabio Pardo, Raia Hadsell, Nicolas Heess, and Josh Merel · 2020
Later among the works it cites.
Acme: A research framework for distributed reinforcement learning
Matt Hoffman, Bobak Shahriari, John Aslanides, Gabriel Barth-Maron, Feryal Behbahani, Tamara Norman, Abbas Abdolmaleki, Albin Cassirer, Fan Yang, Kate Baumli, Sarah Henderson, Alex Novikov, Sergio Gómez Colmenarejo, Serkan Cabi, Caglar Gulcehre, Tom Le Paine, Andrew Cowie, Ziyu Wang, Bilal Piot, and Nando de Freitas · 2020
Later among the works it cites.
Morel: Model-based offline reinforcement learning
Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, and Thorsten Joachims · 2020
Later among the works it cites.
Deep dynamics models for learning dexterous manipulation
Anusha Nagabandi, Kurt Konolige, Sergey Levine, and Vikash Kumar · 2020
Later among the works it cites.
Mastering atari, go, chess and shogi by planning with a learned model
Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, et al · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Dual policy iteration
Wen Sun, Geoffrey J Gordon, Byron Boots, and J Bagnell · 2018
Cited alongside, same era.
Yuval Tassa, Yotam Doron, Alistair Muldal, Tom Erez, Yazhe Li, Diego de Las Casas, David Budden, Abbas Abdolmaleki, Josh Merel, Andrew Lefrancq, et al · 2018
Cited alongside, same era.
Information asymmetry in KL-regularized RL
Alexandre Galashov, Siddhant Jayakumar, Leonard Hasenclever, Dhruva Tirumala, Jonathan Schwarz, Guillaume Desjardins, Wojtek M Czarnecki, Yee Whye Teh, Razvan Pascanu, and Nicolas Heess · 2019
Cited alongside, same era.
Feedback mpc for torque-controlled legged robots
Ruben Grandia, Farbod Farshidian, René Ranftl, and Marco Hutter · 2019
Cited alongside, same era.
When to trust your model: Model-Based policy optimization
Michael Janner, Justin Fu, Marvin Zhang, and Sergey Levine · 2019
Cited alongside, same era.
Plan online, learn offline: Efficient learning and exploration via model-based control
Kendall Lowrey, Aravind Rajeswaran, Sham Kakade, Emanuel Todorov, and Igor Mordatch · 2019
Cited alongside, same era.
Probabilistic planning with sequential monte carlo methods
Alexandre Piché, Valentin Thomas, Cyril Ibrahim, Yoshua Bengio, and Chris Pal · 2019
Cited alongside, same era.
Local search for policy iteration in continuous control
Jost Tobias Springenberg, Nicolas Heess, Daniel Mankowitz, Josh Merel, Arunkumar Byravan, Abbas Abdolmaleki, Jackie Kay, Jonas Degrave, Julian Schrittwieser, Yuval Tassa, et al · 2020
Later among the works it cites.
dm_control: Software and tasks for continuous control
Saran Tunyasuvunakool, Alistair Muldal, Yotam Doron, Siqi Liu, Steven Bohez, Josh Merel, Tom Erez, Timothy Lillicrap, Nicolas Heess, and Yuval Tassa · 2020
Later among the works it cites.
Exploring model-based planning with policy networks
Tingwu Wang and Jimmy Ba · 2020
Later among the works it cites.
http://mocap.cs.cmu.edu/
Carnegie mellon university - CMU graphics lab - motion capture library · 2021
Closest in time.
Model-based offline planning
Arthur Argenson and Gabriel Dulac-Arnold · 2021
Closest in time.
Blending mpc & value function approximation for efficient reinforcement learning
Mohak Bhardwaj, Sanjiban Choudhury, and Byron Boots · 2021
Closest in time.
Reverb: A framework for experience replay, February 2021
Albin Cassirer, Gabriel Barth-Maron, Eugene Brevdo, Sabela Ramos, Toby Boyd, Thibault Sottiaux, and Manuel Kroiss · 2021
Closest in time.
On the role of planning in model-based deep reinforcement learning
Jessica B Hamrick, Abram L Friesen, Feryal Behbahani, Arthur Guez, Fabio Viola, Sims Witherspoon, Thomas Anthony, Lars Buesing, Petar Veličković, and Théophane Weber · 2021
Closest in time.
Learning and planning in complex action spaces
Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Mohammadamin Barekatain, Simon Schmitt, and David Silver · 2021
Closest in time.
Learning dynamics models for model predictive agents
Michael Lutter, Leonard Hasenclever, Arunkumar Byravan, Gabriel Dulac-Arnold, Piotr Trochim, Nicolas Heess, Josh Merel, and Yuval Tassa · 2021
Closest in time.
A unified mpc framework for whole-body dynamic locomotion and manipulation
Jean-Pierre Sleiman, Farbod Farshidian, Maria Vittoria Minniti, and Marco Hutter · 2021
Closest in time.
Data-driven mpc for quadrotors
Guillem Torrente, Elia Kaufmann, Philipp Föhn, and Davide Scaramuzza · 2021
Closest in time.
Skill transfer via partially amortized hierarchical planning
Kevin Xie, Homanga Bharadhwaj, Danijar Hafner, Animesh Garg, and Florian Shkurti · 2021
Closest in time.