Fetching the paper…
Reading the bibliography…
To handle the sparsity of the extrinsic rewards in reinforcement learning, researchers have proposed intrinsic reward which enables the agent to learn the skills that might come in handy for pursuing the rewards in the future, such as encouraging the agent to visit novel states.
A possibility for implementing curiosity and boredom in model-building neural controllers
Jürgen Schmidhuber · 1991
Earlier work this paper cites.
Intrinsic and extrinsic motivations: Classic definitions and new directions
Richard M Ryan and Edward L Deci · 2000
Earlier work this paper cites.
R-max-a general polynomial time algorithm for near-optimal reinforcement learning
Ronen I Brafman and Moshe Tennenholtz · 2002
Earlier work this paper cites.
Matrix rank minimization with applications
Maryam Fazel · 2002
Earlier work this paper cites.
Near-optimal reinforcement learning in polynomial time
Michael Kearns and Satinder Singh · 2002
Earlier work this paper cites.
Intrinsically motivated reinforcement learning
Nuttapong Chentanez, Andrew Barto, and Satinder Singh · 2004
Earlier work this paper cites.
The development of embodied cognition: Six lessons from babies
Linda Smith and Michael Gasser · 2005
Earlier work this paper cites.
The arcade learning environment: An evaluation platform for general agents
Marc G Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling · 2013
Earlier work this paper cites.
Active contours with group similarity
Xiaowei Zhou, Xiaojie Huang, James S Duncan, and Weichuan Yu · 2013
Earlier work this paper cites.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
Earlier work this paper cites.
Incentivizing exploration in reinforcement learning with deep predictive models
Bradly C Stadie, Sergey Levine, and Pieter Abbeel · 2015
Earlier work this paper cites.
Unifying count-based exploration and intrinsic motivation
Marc Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, and Remi Munos · 2016
Earlier work this paper cites.
Count-based exploration in feature space for reinforcement learning
Jarryd Martin, S. Suraj Narayanan, Tom Everitt, and Marcus Hutter · 2017
Earlier work this paper cites.
Count-based exploration with neural density models
Georg Ostrovski, Marc G Bellemare, Aäron Oord, and Rémi Munos · 2017
Cited alongside, same era.
Curiosity-driven exploration by self-supervised prediction
Deepak Pathak, Pulkit Agrawal, Alexei A Efros, and Trevor Darrell · 2017
Cited alongside, same era.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Cited alongside, same era.
Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al · 2017
Cited alongside, same era.
Large-scale study of curiosity-driven learning
Yuri Burda, Harri Edwards, Deepak Pathak, Amos Storkey, Trevor Darrell, and Alexei A Efros · 2018
Cited alongside, same era.
Towards discriminability and diversity: Batch nuclear-norm maximization under label insufficient situations
Shuhao Cui, Shuhui Wang, Junbao Zhuo, Liang Li, Qingming Huang, and Qi Tian · 2020
Later among the works it cites.
See, hear, explore: Curiosity via audio-visual association
Victoria Dean, Shubham Tulsiani, and Abhinav Gupta · 2020
Later among the works it cites.
An empirical study of alternating least squares collaborative filtering recommendation for movielens on apache hadoop and spark
Jung-Bin Li, Szu-Yin Lin, Yu-Hsiang Hsu, and Ying-Chu Huang · 2020
Later among the works it cites.
Data-efficient reinforcement learning with self-predictive representations
Max Schwarzer, Ankesh Anand, Rishab Goel, R Devon Hjelm, Aaron Courville, and Philip Bachman · 2020
Later among the works it cites.
Novelty search in representational space for sample efficient exploration
Ruo Yu Tao, Vincent François-Lavet, and Joelle Pineau · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Matteo Hessel, Joseph Modayil, Hado Van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, and David Silver · 2018
Cited alongside, same era.
Hierarchical deep reinforcement learning for continuous action control
Zhaoyang Yang, Kathryn Merrick, Lianwen Jin, and Hussein A Abbass · 2018
Cited alongside, same era.
Never give up: Learning directed exploration strategies
Adrià Puigdomènech Badia, Pablo Sprechmann, Alex Vitvitskyi, Daniel Guo, Bilal Piot, Steven Kapturowski, Olivier Tieleman, Martin Arjovsky, Alexander Pritzel, Andrew Bolt, et al · 2019
Cited alongside, same era.
Exploration by random network distillation
Yuri Burda, Harrison Edwards, Amos Storkey, and Oleg Klimov · 2019
Cited alongside, same era.
Factor group-sparse regularization for efficient low-rank matrix recovery
Jicong Fan, Lijun Ding, Yudong Chen, and Madeleine Udell · 2019
Cited alongside, same era.
Self-supervised exploration via disagreement
Deepak Pathak, Dhiraj Gandhi, and Abhinav Gupta · 2019
Cited alongside, same era.
Why are big data matrices approximately low rank?
Madeleine Udell and Alex Townsend · 2019
Cited alongside, same era.
dm_control: Software and tasks for continuous control
Saran Tunyasuvunakool, Alistair Muldal, Yotam Doron, Siqi Liu, Steven Bohez, Josh Merel, Tom Erez, Timothy Lillicrap, Nicolas Heess, and Yuval Tassa · 2020
Later among the works it cites.
Image augmentation is all you need: Regularizing deep reinforcement learning from pixels
Denis Yarats, Ilya Kostrikov, and Rob Fergus · 2020
Later among the works it cites.
Low rank regularization: A review
Zhanxuan Hu, Feiping Nie, Rong Wang, and Xuelong Li · 2021
Later among the works it cites.
Urlb: Unsupervised reinforcement learning benchmark
Michael Laskin, Denis Yarats, Hao Liu, Kimin Lee, Albert Zhan, Kevin Lu, Catherine Cang, Lerrel Pinto, and Pieter Abbeel · 2021
Later among the works it cites.
Sure based truncated tensor nuclear norm regularization for low rank tensor completion
Gordon Morison · 2021
Later among the works it cites.
Wrmatch: Improving fixmatch with weighted nuclear-norm regularization for few-shot remote sensing scene classification
Yunsheng Xiong, Kele Xu, Yong Dou, Yang Zhao, and Zikai Gao · 2021
Later among the works it cites.
Reinforcement learning with prototypical representations
Denis Yarats, Rob Fergus, Alessandro Lazaric, and Lerrel Pinto · 2021
Later among the works it cites.
Sample efficient deep reinforcement learning via uncertainty estimation
Vincent Mai, Kaustubh Mani, and Liam Paull · 2022
Closest in time.