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Multi-task learning is a very challenging problem in reinforcement learning.
Adaptive mixture of local expert
Robert Jacobs, Michael Jordan, Steven Nowlan, and Geoffrey Hinton · 1991
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Transfer of learning by composing solutions of elemental sequential tasks
Satinder Pal Singh · 1992
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Recognition of manipulated objects by motor learning with modular architecture networks
Hiroaki Gomi and Mitsuo Kawato · 1993
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Hierarchical mixtures of experts and the em algorithm
M. I. Jordan and R. A. Jacobs · 1993
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Mixtures of controllers for jump linear and non-linear plants
Timothy W. Cacciatore and Steven J. Nowlan · 1994
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Multitask learning
Rich Caruana · 1997
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Multi-task reinforcement learning: a hierarchical bayesian approach
Aaron Wilson, Alan Fern, Soumya Ray, and Prasad Tadepalli · 2007
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Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
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Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
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A regularization approach to learning task relationships in multitask learning
Yu Zhang and Dit-Yan Yeung · 2014
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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 · 2015
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Actor-mimic: Deep multitask and transfer reinforcement learning
Emilio Parisotto, Jimmy Lei Ba, and Ruslan Salakhutdinov · 2015
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Andrei A Rusu, Sergio Gomez Colmenarejo, Caglar Gulcehre, Guillaume Desjardins, James Kirkpatrick, Razvan Pascanu, Volodymyr Mnih, Koray Kavukcuoglu, and Raia Hadsell · 2015
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End-to-end training of deep visuomotor policies
Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel · 2016
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The curious robot: Learning visual representations via physical interactions
Lerrel Pinto, Dhiraj Gandhi, Yuanfeng Han, Yong-Lae Park, and Abhinav Gupta · 2016
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Andrei A Rusu, Neil C Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, and Raia Hadsell · 2016
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Modular multitask reinforcement learning with policy sketches
Jacob Andreas, Dan Klein, and Sergey Levine · 2017
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Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks
Zhao Chen, Vijay Badrinarayanan, Chen-Yu Lee, and Andrew Rabinovich · 2017
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Learning modular neural network policies for multi-task and multi-robot transfer
Coline Devin, Abhishek Gupta, Trevor Darrell, Pieter Abbeel, and Sergey Levine · 2017
Learning by playing-solving sparse reward tasks from scratch
Martin Riedmiller, Roland Hafner, Thomas Lampe, Michael Neunert, Jonas Degrave, Tom Van de Wiele, Volodymyr Mnih, Nicolas Heess, and Jost Tobias Springenberg · 2018
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Multi-task learning as multi-objective optimization
Ozan Sener and Vladlen Koltun · 2018
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Learning anytime predictions in neural networks via adaptive loss balancing
Hanzhang Hu, Debadeepta Dey, Martial Hebert, and J Andrew Bagnell · 2019
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Adaptive auxiliary task weighting for reinforcement learning
Xingyu Lin, Harjatin Baweja, George Kantor, and David Held · 2019
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Mcp: Learning composable hierarchical control with multiplicative compositional policies
Xue Bin Peng, Michael Chang, Grace Zhang, Pieter Abbeel, and Sergey Levine · 2019
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Learning to push by grasping: Using multiple tasks for effective learning
Lerrel Pinto and Abhinav Gupta · 2017
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Routing networks: Adaptive selection of non-linear functions for multi-task learning
Clemens Rosenbaum, Tim Klinger, and Matthew Riemer · 2017
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Himanshu Sahni, Saurabh Kumar, Farhan Tejani, and Charles Isbell · 2017
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Distral: Robust multitask reinforcement learning
Yee Teh, Victor Bapst, Wojciech M Czarnecki, John Quan, James Kirkpatrick, Raia Hadsell, Nicolas Heess, and Razvan Pascanu · 2017
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Adapting auxiliary losses using gradient similarity
Yunshu Du, Wojciech M Czarnecki, Siddhant M Jayakumar, Razvan Pascanu, and Balaji Lakshminarayanan · 2018
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Composable deep reinforcement learning for robotic manipulation
Tuomas Haarnoja, Vitchyr Pong, Aurick Zhou, Murtaza Dalal, Pieter Abbeel, and Sergey Levine · 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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Senthil Purushwalkam, Maximilian Nickel, Abhinav Gupta, and Marc’Aurelio Ranzato · 2019
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Routing networks and the challenges of modular and compositional computation
Clemens Rosenbaum, Ignacio Cases, Matthew Riemer, and Tim Klinger · 2019
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Learning to navigate using mid-level visual priors
Alexander Sax, Jeffrey O Zhang, Bradley Emi, Amir Zamir, Silvio Savarese, Leonidas Guibas, and Jitendra Malik · 2019
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Tafe-net: Task-aware feature embeddings for low shot learning
Xin Wang, Fisher Yu, Ruth Wang, Trevor Darrell, and Joseph E Gonzalez · 2019
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Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning
Tianhe Yu, Deirdre Quillen, Zhanpeng He, Ryan Julian, Karol Hausman, Chelsea Finn, and Sergey Levine · 2019
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Reinforcement learning with competitive ensembles of information-constrained primitives
Anirudh Goyal, Shagun Sodhani, Jonathan Binas, Xue Bin Peng, Sergey Levine, and Yoshua Bengio · 2020
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Learning dynamic routing for semantic segmentation
Yanwei Li, Lin Song, Yukang Chen, Zeming Li, Xiangyu Zhang, Xingang Wang, and Jian Sun · 2020
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Composing task-agnostic policies with deep reinforcement learning
Ahmed H. Qureshi, Jacob J. Johnson, Yuzhe Qin, Taylor Henderson, Byron Boots, and Michael C. Yip · 2020
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Gradient surgery for multi-task learning
Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, and Chelsea Finn · 2020
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