Fetching the paper…
Reading the bibliography…
Models used in modern planning problems to simulate outcomes of real world action executions are becoming increasingly complex, ranging from simulators that do physics-based reasoning to precomputed analytical motion primitives.
Real-Time Heuristic Search
Richard E. Korf · 1990
Earlier work this paper cites.
Dyna, an Integrated Architecture for Learning, Planning, and Reacting
Richard S. Sutton · 1991
Earlier work this paper cites.
Complexity Analysis of Real-Time Reinforcement Learning
Sven Koenig and Reid G. Simmons · 1993
Earlier work this paper cites.
Learning to Act Using Real-Time Dynamic Programming
Andrew G. Barto, Steven J. Bradtke, and Satinder P. Singh · 1995
Earlier work this paper cites.
Learning tasks from a single demonstration
Christopher G. Atkeson and Stefan Schaal · 1997
Earlier work this paper cites.
Locally Weighted Learning for Control
Christopher G. Atkeson, Andrew W. Moore, and Stefan Schaal · 1997
Earlier work this paper cites.
Locally Weighted Projection Regression: Incremental Real Time Learning in High Dimensional Space
Sethu Vijayakumar and Stefan Schaal · 2000
Earlier work this paper cites.
Randomized Kinodynamic Planning
Steven M. LaValle and James J. Kuffner Jr · 2001
Earlier work this paper cites.
R-MAX - A General Polynomial Time Algorithm for Near-Optimal Reinforcement Learning
Ronen I. Brafman and Moshe Tennenholtz · 2002
Earlier work this paper cites.
Near-Optimal Reinforcement Learning in Polynomial Time
Michael J. Kearns and Satinder P. Singh · 2002
Earlier work this paper cites.
A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes
Michael J. Kearns, Yishay Mansour, and Andrew Y. Ng · 2002
Earlier work this paper cites.
Exploration in Metric State Spaces
Sham M. Kakade, Michael J. Kearns, and John Langford · 2003
Cited alongside, same era.
Dynamic programming and optimal control, 3rd Edition
Dimitri P. Bertsekas · 2005
Cited alongside, same era.
Using inaccurate models in reinforcement learning
Pieter Abbeel, Morgan Quigley, and Andrew Y. Ng · 2006
Cited alongside, same era.
Using Motion Primitives in Probabilistic Sample-Based Planning for Humanoid Robots
Kris K. Hauser, Timothy Bretl, Kensuke Harada, and Jean-Claude Latombe · 2006
Cited alongside, same era.
Real-time adaptive A*
Sven Koenig and Maxim Likhachev · 2006
Cited alongside, same era.
Model-based function approximation in reinforcement learning
Nicholas K. Jong and Peter Stone · 2007
Cited alongside, same era.
Gaussian Processes for Data-Efficient Learning in Robotics and Control
Marc Peter Deisenroth, Dieter Fox, and Carl Edward Rasmussen · 2013
Later among the works it cites.
Reducing hardware experiments for model learning and policy optimization
Sehoon Ha and Katsu Yamane · 2015
Later among the works it cites.
Adam: A Method for Stochastic Optimization
Diederik P. Kingma and Jimmy Ba · 2015
Later among the works it cites.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin A. Riedmiller, Andreas Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis · 2015
Later among the works it cites.
Data-efficient control policy search using residual dynamics learning
Matteo Saveriano, Yuchao Yin, Pietro Falco, and Dongheui Lee · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Learning for control from multiple demonstrations
Adam Coates, Pieter Abbeel, and Andrew Y. Ng · 2008
Cited alongside, same era.
Multi-resolution Exploration in Continuous Spaces
Ali Nouri and Michael L. Littman · 2008
Cited alongside, same era.
Adaptive-resolution reinforcement learning with polynomial exploration in deterministic domains
Andrey Bernstein and Nahum Shimkin · 2010
Cited alongside, same era.
Planning for Manipulation with Adaptive Motion Primitives
Benjamin J. Cohen, Gokul Subramania, Sachin Chitta, and Maxim Likhachev · 2011
Cited alongside, same era.
MuJoCo: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
Cited alongside, same era.
Modeling cooperative navigation in dense human crowds
Anirudh Vemula, Katharina Mülling, and Jean Oh · 2017
Later among the works it cites.
PAC Reinforcement Learning With an Imperfect Model
Nan Jiang · 2018
Later among the works it cites.
Sample-efficient reinforcement learning via difference models
Divyam Rastogi, Ivan Koryakovskiy, and Jens Kober · 2018
Later among the works it cites.
When to Trust Your Model: Model-Based Policy Optimization
Michael Janner, Justin Fu, Marvin Zhang, and Sergey Levine · 2019
Later among the works it cites.
Learning When to Trust a Dynamics Model for Planning in Reduced State Spaces
Dale McConachie, Thomas Power, Peter Mitrano, and Dmitry Berenson · 2020
Closest in time.