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Goal-conditioned reinforcement learning (RL) can solve tasks in a wide range of domains, including navigation and manipulation, but learning to reach distant goals remains a central challenge to the field.
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Arthur P Dempster, Nan M Laird, and Donald B Rubin · 1977
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The general problem of the stability of motion
Aleksandr Mikhailovich Lyapunov · 1992
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Leslie Pack Kaelbling · 1993
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Approximately optimal approximate reinforcement learning
Sham Kakade and John Langford · 2002
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Planning by probabilistic inference
Hagai Attias · 2003
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On the probabilistic foundations of probabilistic roadmap planning
David Hsu, Jean-Claude Latombe, and Hanna Kurniawati · 2006
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Linearly-solvable markov decision problems
Emanuel Todorov · 2007
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A generalized path integral control approach to reinforcement learning
Evangelos Theodorou, Jonas Buchli, and Stefan Schaal · 2010
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Modeling purposeful adaptive behavior with the principle of maximum causal entropy
Brian D Ziebart · 2010
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On stochastic optimal control and reinforcement learning by approximate inference
Konrad Rawlik, Marc Toussaint, and Sethu Vijayakumar · 2013
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Generative adversarial imitation learning
Jonathan Ho and Stefano Ermon · 2016
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Reverse curriculum generation for reinforcement learning
Carlos Florensa, David Held, Markus Wulfmeier, Michael Zhang, and Pieter Abbeel · 2017
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Leave no trace: Learning to reset for safe and autonomous reinforcement learning
Benjamin Eysenbach, Shixiang Gu, 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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Reinforcement learning and control as probabilistic inference: Tutorial and review
Sergey Levine · 2018
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Semi-parametric topological memory for navigation
Nikolay Savinov, Alexey Dosovitskiy, and Vladlen Koltun · 2018
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Goal-conditioned imitation learning
Yiming Ding, Carlos Florensa, Pieter Abbeel, and Mariano Phielipp · 2019
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Search on the replay buffer: Bridging planning and reinforcement learning
Benjamin Eysenbach, Ruslan Salakhutdinov, and Sergey Levine · 2019
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Hyperbolic discounting and learning over multiple horizons
William Fedus, Carles Gelada, Yoshua Bengio, Marc G Bellemare, and Hugo Larochelle · 2019
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Offline reinforcement learning: Tutorial, review, and perspectives on open problems
Sergey Levine, Aviral Kumar, George Tucker, and Justin Fu · 2020
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Learning latent plans from play
Corey Lynch, Mohi Khansari, Ted Xiao, Vikash Kumar, Jonathan Tompson, Sergey Levine, and Pierre Sermanet · 2020
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Scaling local control to large-scale topological navigation
Xiangyun Meng, Nathan Ratliff, Yu Xiang, and Dieter Fox · 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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Skew-fit: State-covering self-supervised reinforcement learning
Vitchyr Pong, Murtaza Dalal, Steven Lin, Ashvin Nair, Shikhar Bahl, and Sergey Levine · 2020
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Zhiao Huang, Fangchen Liu, and Hao Su · 2019
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Reinforcement learning without ground-truth state
Xingyu Lin, Harjatin Singh Baweja, and David Held · 2019
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Dualdice: Behavior-agnostic estimation of discounted stationary distribution corrections
Ofir Nachum, Yinlam Chow, Bo Dai, and Lihong Li · 2019
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Planning with goal-conditioned policies
Soroush Nasiriany, Vitchyr Pong, Steven Lin, and Sergey Levine · 2019
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Exploration via hindsight goal generation
Zhizhou Ren, Kefan Dong, Yuan Zhou, Qiang Liu, and Jian Peng · 2019
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C-learning: Learning to achieve goals via recursive classification
Benjamin Eysenbach, Ruslan Salakhutdinov, and Sergey Levine · 2020
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Relay policy learning: Solving long-horizon tasks via imitation and reinforcement learning
Abhishek Gupta, Vikash Kumar, Corey Lynch, Sergey Levine, and Karol Hausman · 2020
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Yannick Schroecker and Charles Isbell · 2020
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Ving: Learning open-world navigation with visual goals
Dhruv Shah, Benjamin Eysenbach, Gregory Kahn, Nicholas Rhinehart, and Sergey Levine · 2020
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Cog: Connecting new skills to past experience with offline reinforcement learning
Avi Singh, Albert Yu, Jonathan Yang, Jesse Zhang, Aviral Kumar, and Sergey Levine · 2020
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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 · 2020
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Bebold: Exploration beyond the boundary of explored regions
Tianjun Zhang, Huazhe Xu, Xiaolong Wang, Yi Wu, Kurt Keutzer, Joseph E Gonzalez, and Yuandong Tian · 2020
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On the theory of policy gradient methods: Optimality, approximation, and distribution shift
Alekh Agarwal, Sham M Kakade, Jason D Lee, and Gaurav Mahajan · 2021
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Goal-conditioned reinforcement learning with imagined subgoals
Elliot Chane-Sane, Cordelia Schmid, and Ivan Laptev · 2021
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Made: Exploration via maximizing deviation from explored regions
Tianjun Zhang, Paria Rashidinejad, Jiantao Jiao, Yuandong Tian, Joseph Gonzalez, and Stuart Russell · 2021
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