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Transfer in Reinforcement Learning (RL) refers to the idea of applying knowledge gained from previous tasks to solving related tasks.
Improving generalization for temporal difference learning: The successor representation
Peter Dayan · 1993
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Generalization in reinforcement learning: Successful examples using sparse coarse coding
Richard S Sutton · 1996
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Multitask learning
Rich Caruana · 1997
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Transfer learning for reinforcement learning domains: A survey
Matthew E Taylor and Peter Stone · 2009
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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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Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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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, et al · 2015
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Universal value function approximators
Tom Schaul, Daniel Horgan, Karol Gregor, and David Silver · 2015
Cited alongside, same era.
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
Cited alongside, same era.
Reinforcement learning with unsupervised auxiliary tasks
Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z Leibo, David Silver, and Koray Kavukcuoglu · 2016
Cited alongside, same era.
Deep successor reinforcement learning
Tejas D Kulkarni, Ardavan Saeedi, Simanta Gautam, and Samuel J Gershman · 2016
Cited alongside, same era.
End-to-end training of deep visuomotor policies
Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel · 2016
Cited alongside, same era.
Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
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Hindsight experience replay
Marcin Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, Pieter Abbeel, and Wojciech Zaremba · 2017
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Successor features for transfer in reinforcement learning
André Barreto, Will Dabney, Rémi Munos, Jonathan J Hunt, Tom Schaul, David Silver, and Hado P van Hasselt · 2017
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Transfer in deep reinforcement learning using successor features and generalised policy improvement
Andre Barreto, Diana Borsa, John Quan, Tom Schaul, David Silver, Matteo Hessel, Daniel Mankowitz, Augustin Zidek, and Remi Munos · 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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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
Cited alongside, same era.
Later among the works it cites.
Temporal difference models: Model-free deep RL for model-based control
Vitchyr Pong, Shixiang Gu, Murtaza Dalal, and Sergey Levine · 2018
Later among the works it cites.