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We tackle the Multi-task Batch Reinforcement Learning problem.
The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2008
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Causality
Judea Pearl · 2009
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Causal inference in statistics: An overview
Judea Pearl et al · 2009
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An introduction to causal inference
Judea Pearl · 2010
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Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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Are we ready for autonomous driving? the kitti vision benchmark suite
Andreas Geiger, Philip Lenz, and Raquel Urtasun · 2012
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Trust region policy optimization
John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, and Pieter Abbeel · 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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Actor-mimic: Deep multitask and transfer reinforcement learning
Emilio Parisotto, Jimmy Ba, and Ruslan Salakhutdinov · 2015
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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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Deep reinforcement learning with double q-learning
Hado Van Hasselt, Arthur Guez, and David Silver · 2016
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Learning to reinforcement learn
Jane X. Wang, Zeb Kurth-Nelson, Dhruva Tirumala, Hubert Soyer, Joel Z. Leibo, Rémi Munos, Charles Blundell, Dharshan Kumaran, and Matthew Botvinick · 2016
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Rl$ˆ2$: Fast reinforcement learning via slow reinforcement learning
Yan Duan, John Schulman, Xi Chen, Peter L Bartlett, Ilya Sutskever, and Pieter Abbeel · 2016
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Cyclegan, a master of steganography
Casey Chu, Andrey Zhmoginov, and Mark Sandler · 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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Divide-and-conquer reinforcement learning
Dibya Ghosh, Avi Singh, Aravind Rajeswaran, Vikash Kumar, and Sergey Levine · 2017
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Elements of Causal Inference: Foundations and Learning Algorithms
J. Peters, D. Janzing, and B. Schölkopf · 2017
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In defense of the triplet loss for person re-identification
Alexander Hermans, Lucas Beyer, and Bastian Leibe · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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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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Quan Ho Vuong, Yiming Zhang, and Keith W. Ross · 2018
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Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures
Lasse Espeholt, Hubert Soyer, Remi Munos, Karen Simonyan, Volodymir Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, et al · 2018
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Graph neural networks: A review of methods and applications
Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun · 2018
Cited alongside, same era.
Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
What can neural networks reason about?
Keyulu Xu, Jingling Li, Mozhi Zhang, Simon S Du, Ken-ichi Kawarabayashi, and Stefanie Jegelka · 2019
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Self-training with noisy student improves imagenet classification, 2019
Qizhe Xie, Minh-Thang Luong, Eduard Hovy, and Quoc V. Le · 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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Improving generalization in meta reinforcement learning using learned objectives
Louis Kirsch, Sjoerd van Steenkiste, and Jürgen Schmidhuber · 2019
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Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
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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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Meta reinforcement learning with latent variable gaussian processes
Steindór Sæmundsson, Katja Hofmann, and Marc Peter Deisenroth · 2018
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Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures
Lasse Espeholt, Hubert Soyer, Remi Munos, Karen Simonyan, Volodymir Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, et al · 2018
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On first-order meta-learning algorithms
Alex Nichol, Joshua Achiam, and John Schulman · 2018
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Rein Houthooft, Richard Y. Chen, Phillip Isola, Bradly C. Stadie, Filip Wolski, Jonathan Ho, and Pieter Abbeel · 2018
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Deep reinforcement learning in a handful of trials using probabilistic dynamics models
Kurtland Chua, Roberto Calandra, Rowan McAllister, and Sergey Levine · 2018
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Spinning Up in Deep Reinforcement Learning
Joshua Achiam · 2018
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Jan Humplik, Alexandre Galashov, Leonard Hasenclever, Pedro A Ortega, Yee Whye Teh, and Nicolas Heess · 2019
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Meta reinforcement learning with task embedding and shared policy
Lin Lan, Zhenguo Li, Xiaohong Guan, and Pinghui Wang · 2019
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Fast context adaptation via meta-learning
Luisa Zintgraf, Kyriacos Shiarli, Vitaly Kurin, Katja Hofmann, and Shimon Whiteson · 2019
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Sharing knowledge in multi-task deep reinforcement learning
Carlo D’Eramo, Davide Tateo, Andrea Bonarini, Marcello Restelli, and Jan Peters · 2019
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Meta-amortized variational inference and learning
Kristy Choi, Mike Wu, Noah Goodman, and Stefano Ermon · 2019
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Striving for simplicity and performance in off-policy drl: Output normalization and non-uniform sampling, 2020
Che Wang, Yanqiu Wu, Quan Vuong, and Keith Ross · 2020
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Keep doing what worked: Behavioral modelling priors for offline reinforcement learning
Noah Y Siegel, Jost Tobias Springenberg, Felix Berkenkamp, Abbas Abdolmaleki, Michael Neunert, Thomas Lampe, Roland Hafner, and Martin Riedmiller · 2020
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Varibad: A very good method for bayes-adaptive deep rl via meta-learning
Luisa Zintgraf, Kyriacos Shiarlis, Maximilian Igl, Sebastian Schulze, Yarin Gal, Katja Hofmann, and Shimon Whiteson · 2020
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A meta-transfer objective for learning to disentangle causal mechanisms
Yoshua Bengio, Tristan Deleu, Nasim Rahaman, Nan Rosemary Ke, Sebastien Lachapelle, Olexa Bilaniuk, Anirudh Goyal, and Christopher Pal · 2020
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A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip · 2020
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Self-distillation amplifies regularization in hilbert space, 2020
Hossein Mobahi, Mehrdad Farajtabar, and Peter L. Bartlett · 2020
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Emergent real-world robotic skills via unsupervised off-policy reinforcement learning, 2020
Archit Sharma, Michael Ahn, Sergey Levine, Vikash Kumar, Karol Hausman, and Shixiang Gu · 2020
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D4rl: Datasets for deep data-driven reinforcement learning
Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine · 2020
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Conservative q-learning for offline reinforcement learning
Aviral Kumar, Aurick Zhou, George Tucker, and Sergey Levine · 2020
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Scaling data-driven robotics with reward sketching and batch reinforcement learning
Serkan Cabi, Sergio Gómez Colmenarejo, Alexander Novikov, Ksenia Konyushkova, Scott Reed, Rae Jeong, Konrad Zolna, Yusuf Aytar, David Budden, Mel Vecerík, Oleg Sushkov, David J. P. Barker, Jonathan Scholz, Misha Denil, Nando de Freitas, and Ziyu Wang · 2020
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Revisiting training strategies and generalization performance in deep metric learning
Karsten Roth, Timo Milbich, Samarth Sinha, Prateek Gupta, Bjoern Ommer, and Joseph Paul Cohen · 2020
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Multi-task reinforcement learning with soft modularization
Ruihan Yang, Huazhe Xu, Yi Wu, and Xiaolong Wang · 2020
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