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A good state representation is crucial to solving complicated reinforcement learning (RL) challenges.
Boah: A tool suite for multi-fidelity bayesian optimization & analysis of hyperparameters
M. Lindauer, K. Eggensperger, M. Feurer, A. Biedenkapp, J. Marben, P. Müller, and F. Hutter · 1908
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The arcade learning environment: An evaluation platform for general agents
Marc G Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling · 2013
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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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Incentivizing exploration in reinforcement learning with deep predictive models
Bradly C Stadie, Sergey Levine, and Pieter Abbeel · 2015
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Learning state representation for deep actor-critic control
Jelle Munk, Jens Kober, and Robert Babuška · 2016
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Loss is its own reward: Self-supervision for reinforcement learning
Evan Shelhamer, Parsa Mahmoudieh, Max Argus, and Trevor Darrell · 2016
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Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates
Shixiang Gu, Ethan Holly, Timothy Lillicrap, and Sergey Levine · 2017
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Reinforcement learning with unsupervised auxiliary tasks
Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z. Leibo, David Silver, and Koray Kavukcuoglu · 2017
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Integrating state representation learning into deep reinforcement learning
Tim De Bruin, Jens Kober, Karl Tuyls, and Robert Babuška · 2018
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Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures
Lasse Espeholt, Hubert Soyer, Remi Munos, Karen Simonyan, Vlad Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, et al · 2018
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Soft actor-critic algorithms and applications
Tuomas Haarnoja, Aurick Zhou, Kristian Hartikainen, George Tucker, Sehoon Ha, Jie Tan, Vikash Kumar, Henry Zhu, Abhishek Gupta, Pieter Abbeel, et al · 2018
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Deep reinforcement learning that matters
Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, and David Meger · 2018
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Evolved policy gradients
Rein Houthooft, Yuhua Chen, Phillip Isola, Bradly C. Stadie, Filip Wolski, Jonathan Ho, and Pieter Abbeel · 2018
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Visualizing the loss landscape of neural nets
Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, and Tom Goldstein · 2018
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Yuval Tassa, Yotam Doron, Alistair Muldal, Tom Erez, Yazhe Li, Diego de Las Casas, David Budden, Abbas Abdolmaleki, Josh Merel, Andrew Lefrancq, et al · 2018
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Evolving rewards to automate reinforcement learning
Aleksandra Faust, Anthony Francis, and Dar Mehta · 2019
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An investigation into neural net optimization via hessian eigenvalue density
Behrooz Ghorbani, Shankar Krishnan, and Ying Xiao · 2019
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Dream to control: Learning behaviors by latent imagination
Danijar Hafner, Timothy Lillicrap, Jimmy Ba, and Mohammad Norouzi · 2019
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Learning latent dynamics for planning from pixels
Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, and James Davidson · 2019
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Stochastic latent actor-critic: Deep reinforcement learning with a latent variable model
Alex X Lee, Anusha Nagabandi, Pieter Abbeel, and Sergey Levine · 2019
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Am-lfs: Automl for loss function search
Chuming Li, Xin Yuan, Chen Lin, Minghao Guo, Wei Wu, Junjie Yan, and Wanli Ouyang · 2019
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Fast efficient hyperparameter tuning for policy gradients
Supratik Paul, Vitaly Kurin, and Shimon Whiteson · 2019
Image augmentation is all you need: Regularizing deep reinforcement learning from pixels
Ilya Kostrikov, Denis Yarats, and Rob Fergus · 2020
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Curl: Contrastive unsupervised representations for reinforcement learning
Michael Laskin, Aravind Srinivas, and Pieter Abbeel · 2020
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Reinforcement learning with augmented data
Misha Laskin, Kimin Lee, Adam Stooke, Lerrel Pinto, Pieter Abbeel, and Aravind Srinivas · 2020
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Reinforcement learning with augmented data
Misha Laskin, Kimin Lee, Adam Stooke, Lerrel Pinto, Pieter Abbeel, and Aravind Srinivas · 2020
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Can increasing input dimensionality improve deep reinforcement learning?
Kei Ota, Tomoaki Oiki, Devesh Jha, Toshisada Mariyama, and Daniel Nikovski · 2020
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Regularized evolution for image classifier architecture search
Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V. Le · 2019
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Learning to design RNA
Frederic Runge, Danny Stoll, Stefan Falkner, and Frank Hutter · 2019
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When to use parametric models in reinforcement learning?
Hado P van Hasselt, Matteo Hessel, and John Aslanides · 2019
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Discovery of useful questions as auxiliary tasks
Vivek Veeriah, Matteo Hessel, Zhongwen Xu, Janarthanan Rajendran, Richard L. Lewis, Junhyuk Oh, Hado van Hasselt, David Silver, and Satinder Singh · 2019
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Grandmaster level in starcraft ii using multi-agent reinforcement learning
Oriol Vinyals, Igor Babuschkin, Wojciech M Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H Choi, Richard Powell, Timo Ewalds, Petko Georgiev, et al · 2019
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Improving sample efficiency in model-free reinforcement learning from images
Denis Yarats, Amy Zhang, Ilya Kostrikov, Brandon Amos, Joelle Pineau, and Rob Fergus · 2019
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Nas-bench-101: Towards reproducible neural architecture search, 2019
Chris Ying, Aaron Klein, Esteban Real, Eric Christiansen, Kevin Murphy, and Frank Hutter · 2019
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Max Schwarzer, Ankesh Anand, Rishab Goel, R Devon Hjelm, Aaron Courville, and Philip Bachman · 2020
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Loss function search for face recognition
Xiaobo Wang, Shuo Wang, Cheng Chi, Shifeng Zhang, and Tao Mei · 2020
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Meta-gradient reinforcement learning with an objective discovered online
Zhongwen Xu, Hado Philip van Hasselt, Matteo Hessel, Junhyuk Oh, Satinder Singh, and David Silver · 2020
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A self-tuning actor-critic algorithm
Tom Zahavy, Zhongwen Xu, Vivek Veeriah, Matteo Hessel, Junhyuk Oh, Hado van Hasselt, David Silver, and Satinder Singh · 2020
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Towards deeper deep reinforcement learning
Johan Bjorck, Carla P Gomes, and Kilian Q Weinberger · 2021
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Universal trading for order execution with oracle policy distillation
Yuchen Fang, Kan Ren, Weiqing Liu, Dong Zhou, Weinan Zhang, Jiang Bian, Yong Yu, and Tie-Yan Liu · 2021
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Sample-efficient automated deep reinforcement learning
Jörg K. H. Franke, Gregor Köhler, André Biedenkapp, and Frank Hutter · 2021
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Autoloss-zero: Searching loss functions from scratch for generic tasks
Hao Li, Tianwen Fu, Jifeng Dai, Hongsheng Li, Gao Huang, and Xizhou Zhu · 2021
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Training larger networks for deep reinforcement learning
Kei Ota, Devesh K Jha, and Asako Kanezaki · 2021
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Decoupling representation learning from reinforcement learning
Adam Stooke, Kimin Lee, Pieter Abbeel, and Michael Laskin · 2021
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The shape of learning curves: a review
Tom Viering and Marco Loog · 2021
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Perfectdou: Dominating doudizhu with perfect information distillation
Guan Yang, Minghuan Liu, Weijun Hong, Weinan Zhang, Fei Fang, Guangjun Zeng, and Yue Lin · 2022
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