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Goal-conditioned hierarchical reinforcement learning (GCHRL) provides a promising approach to solving long-horizon tasks.
Why does hierarchy (sometimes) work so well in reinforcement learning?
Ofir Nachum, Haoran Tang, Xingyu Lu, Shixiang Gu, Honglak Lee, and Sergey Levine · 1909
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Improving generalization for temporal difference learning: The successor representation
Peter Dayan · 1993
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Feudal reinforcement learning
Peter Dayan and Geoffrey E Hinton · 1993
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Planning simple trajectories using neural subgoal generators
Jürgen Schmidhuber and Reiner Wahnsiedler · 1993
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To recognize shapes, first learn to generate images
Geoffrey E Hinton · 2007
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An analysis of model-based interval estimation for markov decision processes
Alexander L Strehl and Michael L Littman · 2008
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Remarks on some nonparametric estimates of a density function
Richard A Davis, Keh-Shin Lii, and Dimitris N Politis · 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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Empowerment–an introduction
Christoph Salge, Cornelius Glackin, and Daniel Polani · 2014
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Variational information maximisation for intrinsically motivated reinforcement learning
Shakir Mohamed and Danilo Jimenez Rezende · 2015
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Universal value function approximators
Tom Schaul, Daniel Horgan, Karol Gregor, and David Silver · 2015
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Unifying count-based exploration and intrinsic motivation
Marc G Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, and Remi Munos · 2016
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Deep learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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Karol Gregor, Danilo Jimenez Rezende, and Daan Wierstra · 2016
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Count-based exploration with neural density models
Georg Ostrovski, Marc G Bellemare, Aäron Oord, and Rémi Munos · 2017
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Curiosity-driven exploration by self-supervised prediction
Deepak Pathak, Pulkit Agrawal, Alexei A Efros, and Trevor Darrell · 2017
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# exploration: A study of count-based exploration for deep reinforcement learning
Haoran Tang, Rein Houthooft, Davis Foote, Adam Stooke, Xi Chen, Yan Duan, John Schulman, Filip De Turck, and Pieter Abbeel · 2017
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Feudal networks for hierarchical reinforcement learning
Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul, Nicolas Heess, Max Jaderberg, David Silver, and Koray Kavukcuoglu · 2017
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Large-scale study of curiosity-driven learning
Yuri Burda, Harri Edwards, Deepak Pathak, Amos Storkey, Trevor Darrell, and Alexei A Efros · 2018
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Self-consistent trajectory autoencoder: Hierarchical reinforcement learning with trajectory embeddings
John Co-Reyes, YuXuan Liu, Abhishek Gupta, Benjamin Eysenbach, Pieter Abbeel, and Sergey Levine · 2018
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Diversity is all you need: Learning skills without a reward function
Successor uncertainties: exploration and uncertainty in temporal difference learning
David Janz, Jiri Hron, Przemysław Mazur, Katja Hofmann, José Miguel Hernández-Lobato, and Sebastian Tschiatschek · 2019
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Learning multi-level hierarchies with hindsight
Andrew Levy, George Konidaris, Robert Platt, and Kate Saenko · 2019
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Hierarchical reinforcement learning with advantage-based auxiliary rewards
Siyuan Li, Rui Wang, Minxue Tang, and Chongjie Zhang · 2019
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Hierarchical foresight: Self-supervised learning of long-horizon tasks via visual subgoal generation
Suraj Nair and Chelsea Finn · 2019
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Normalizing flows for probabilistic modeling and inference
George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, and Balaji Lakshminarayanan · 2019
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Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, and Sergey Levine · 2018
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Automatic goal generation for reinforcement learning agents
Carlos Florensa, David Held, Xinyang Geng, and Pieter Abbeel · 2018
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Learning actionable representations with goal-conditioned policies
Dibya Ghosh, Abhishek Gupta, and Sergey Levine · 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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Data-efficient hierarchical reinforcement learning
Ofir Nachum, Shixiang Gu, Honglak Lee, and Sergey Levine · 2018
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Visual reinforcement learning with imagined goals
Ashvin Nair, Vitchyr Pong, Murtaza Dalal, Shikhar Bahl, Steven Lin, and Sergey Levine · 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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Unsupervised learning of goal spaces for intrinsically motivated goal exploration
Alexandre Péré, Sébastien Forestier, Olivier Sigaud, and Pierre-Yves Oudeyer · 2018
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Skew-fit: State-covering self-supervised reinforcement learning
Vitchyr H Pong, Murtaza Dalal, Steven Lin, Ashvin Nair, Shikhar Bahl, and Sergey Levine · 2019
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Dynamics-aware unsupervised discovery of skills
Archit Sharma, Shixiang Gu, Sergey Levine, Vikash Kumar, and Karol Hausman · 2019
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Explore, discover and learn: Unsupervised discovery of state-covering skills
Víctor Campos, Alexander Trott, Caiming Xiong, Richard Socher, Xavier Giro-i Nieto, and Jordi Torres · 2020
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Exploration in reinforcement learning with deep covering options
Yuu Jinnai, Jee Won Park, Marlos C Machado, and George Konidaris · 2020
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Curl: Contrastive unsupervised representations for reinforcement learning
Michael Laskin, Aravind Srinivas, and Pieter Abbeel · 2020
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Count-based exploration with the successor representation
Marlos C Machado, Marc G Bellemare, and Michael Bowling · 2020
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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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Curious hierarchical actor-critic reinforcement learning
Frank Röder, Manfred Eppe, Phuong DH Nguyen, and Stefan Wermter · 2020
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Generating adjacency-constrained subgoals in hierarchical reinforcement learning
Tianren Zhang, Shangqi Guo, Tian Tan, Xiaolin Hu, and Feng Chen · 2020
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Learning subgoal representations with slow dynamics
Siyuan Li, Lulu Zheng, Jianhao Wang, and Chongjie Zhang · 2021
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