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The dominant framework for off-policy multi-goal reinforcement learning involves estimating goal conditioned Q-value function.
Q-learning
Christopher JCH Watkins and Peter Dayan · 1992
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Improving generalization for temporal difference learning: The successor representation
Peter Dayan · 1993
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Learning to achieve goals
Leslie Pack Kaelbling · 1993
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Bootstrap confidence intervals
Thomas J DiCiccio and Bradley Efron · 1996
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Predictive representations of state
M Littman, R Sutton, and Satinder Singh · 2001
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Learning predictive state representations
Satinder P Singh, Michael L Littman, Nicholas K Jong, David Pardoe, and Peter Stone · 2003
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Value function approximation with diffusion wavelets and laplacian eigenfunctions
Sridhar Mahadevan and Mauro Maggioni · 2006
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Proto-value functions: A laplacian framework for learning representation and control in markov decision processes
Sridhar Mahadevan and Mauro Maggioni · 2007
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Horde: A scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction
Richard S Sutton, Joseph Modayil, Michael Delp, Thomas Degris, Patrick M Pilarski, Adam White, and Doina Precup · 2011
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Deterministic policy gradient algorithms
David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, and Martin Riedmiller · 2014
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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
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Universal value function approximators
Tom Schaul, Daniel Horgan, Karol Gregor, and David Silver · 2015
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Deep successor reinforcement learning
Tejas D. Kulkarni, Ardavan Saeedi, Simanta Gautam, and Samuel J. Gershman · 2016
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Learning to navigate in complex environments
Piotr Mirowski, Razvan Pascanu, Fabio Viola, Hubert Soyer, Andy Ballard, Andrea Banino, Misha Denil, Ross Goroshin, Laurent Sifre, Koray Kavukcuoglu, et al · 2017
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Addressing function approximation error in actor-critic methods
Scott Fujimoto, Herke van Hoof, and David Meger · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
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Multi-goal reinforcement learning: Challenging robotics environments and request for research
Matthias Plappert, Marcin Andrychowicz, Alex Ray, Bob McGrew, Bowen Baker, Glenn Powell, Jonas Schneider, Josh Tobin, Maciek Chociej, Peter Welinder, et al · 2018
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Reinforcement Learning: An Introduction
Richard S Sutton and Andrew G Barto · 2018
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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
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Hindsight experience replay
Marcin Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, OpenAI Pieter Abbeel, and Wojciech Zaremba · 2017
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Universal successor features approximators
Diana Borsa, André Barreto, John Quan, Daniel Mankowitz, Rémi Munos, Hado van Hasselt, David Silver, and Tom Schaul · 2019
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Maximum entropy gain exploration for long horizon multi-goal reinforcement learning
Silviu Pitis, Harris Chan, Stephen Zhao, Bradly Stadie, and Jimmy Ba · 2020
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