Fetching the paper…
Reading the bibliography…
Unsupervised skill learning objectives (Gregor et al., 2016, Eysenbach et al., 2018) allow agents to learn rich repertoires of behavior in the absence of extrinsic rewards.
Solving Rubik’s Cube with a robot hand
OpenAI, Ilge Akkaya, Marcin Andrychowicz, Maciek Chociej, Mateusz Litwin, Bob McGrew, Arthur Petron, Alex Paino, Matthias Plappert, Glenn Powell, Raphael Ribas, Jonas Schneider, Nikolas Tezak, Jerry Tworek, Peter Welinder, Lilian Weng, Qiming Yuan, Wojciech Zaremba, and Lei Zhang · 1910
Earlier work this paper cites.
Dota 2 with large scale deep reinforcement learning
OpenAI, Christopher Berner, Greg Brockman, Brooke Chan, Vicki Cheung, Przemysław Dębiak, Christy Dennison, David Farhi, Quirin Fischer, Shariq Hashme, Chris Hesse, Rafal Józefowicz, Scott Gray, Catherine Olsson, Jakub Pachocki, Michael Petrov, Henrique P. d. O. Pinto, Jonathan Raiman, Tim Salimans, Jeremy Schlatter, Jonas Schneider, Szymon Sidor, Ilya Sutskever, Jie Tang, Filip Wolski, and Susan Zhang · 1912
Earlier work this paper cites.
Query by committee
H. Sebastian Seung, Manfred Opper, and Haim Sompolinsky · 1992
Earlier work this paper cites.
Incremental multi-step Q-learning
Jing Peng and Ronald J Williams · 1994
Earlier work this paper cites.
Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning
Richard S. Sutton, Doina Precup, and Satinder Singh · 1999
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.
Information maximization in noisy channels: A variational approach
David Barber and Felix Agakov · 2004
Earlier work this paper cites.
The Arcade Learning Environment: An evaluation platform for general agents
M. G. Bellemare, Y. Naddaf, J. Veness, and M. Bowling · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 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.
Karol Gregor, Danilo Jimenez Rezende, and Daan Wierstra · 2016
Earlier work this paper cites.
Deep exploration via bootstrapped DQN
Ian Osband, Charles Blundell, Alexander Pritzel, and Benjamin Van Roy · 2016
Earlier work this paper cites.
UCB exploration via Q-ensembles
Richard Y Chen, Szymon Sidor, Pieter Abbeel, and John Schulman · 2017
Earlier work this paper cites.
Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
Earlier work this paper cites.
Variational option discovery algorithms
Joshua Achiam, Harrison Edwards, Dario Amodei, and Pieter Abbeel · 2018
Cited alongside, same era.
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, Shane Legg, and Koray Kavukcuoglu · 2018
Cited alongside, same era.
Randomized prior functions for deep reinforcement learning
Ian Osband, John Aslanides, and Albin Cassirer · 2018
Cited alongside, same era.
Unsupervised state representation learning in Atari
Ankesh Anand, Evan Racah, Sherjil Ozair, Yoshua Bengio, Marc-Alexandre Côté, and R Devon Hjelm · 2019
Cited alongside, same era.
Exploration by random network distillation
Yuri Burda, Harrison Edwards, Amos Storkey, and Oleg Klimov · 2019
Cited alongside, same era.
Agent57: Outperforming the Atari human benchmark
Adrià Puigdomènech Badia, Bilal Piot, Steven Kapturowski, Pablo Sprechmann, Alex Vitvitskyi, Zhaohan Daniel Guo, and Charles Blundell · 2020
Later among the works it cites.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
Later among the works it cites.
Temporal difference uncertainties as a signal for exploration
Sebastian Flennerhag, Jane X Wang, Pablo Sprechmann, Francesco Visin, Alexandre Galashov, Steven Kapturowski, Diana L Borsa, Nicolas Heess, Andre Barreto, and Razvan Pascanu · 2020
Later among the works it cites.
Fast task inference with variational intrinsic successor features
Steven Hansen, Will Dabney, Andre Barreto, David Warde-Farley, Tom Van de Wiele, and Volodymyr Mnih · 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…
Diversity is all you need: Learning skills without a reward function
Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, and Sergey Levine · 2019
Cited alongside, same era.
Recurrent experience replay in distributed reinforcement learning
Steven Kapturowski, Georg Ostrovski, Will Dabney, John Quan, and Remi Munos · 2019
Cited alongside, same era.
MAVEN: Multi-agent variational exploration
Anuj Mahajan, Tabish Rashid, Mikayel Samvelyan, and Shimon Whiteson · 2019
Cited alongside, same era.
Deep exploration via randomized value functions
Ian Osband, Benjamin Van Roy, Daniel J. Russo, and Zheng Wen · 2019
Cited alongside, same era.
Self-supervised exploration via disagreement
Deepak Pathak, Dhiraj Gandhi, and Abhinav Gupta · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
Cited alongside, same era.
Model-based active exploration
Pranav Shyam, Wojciech Jaśkowski, and Faustino Gomez · 2019
Cited alongside, same era.
Chi Jin, Akshay Krishnamurthy, Max Simchowitz, and Tiancheng Yu · 2020
Later among the works it cites.
One solution is not all you need: Few-shot extrapolation via structured maxent RL
Saurabh Kumar, Aviral Kumar, Sergey Levine, and Chelsea Finn · 2020
Later among the works it cites.
RIDE: Rewarding impact-driven exploration for procedurally-generated environments
Roberta Raileanu and Tim Rocktäschel · 2020
Later among the works it cites.
Planning to explore via self-supervised world models
Ramanan Sekar, Oleh Rybkin, Kostas Daniilidis, Pieter Abbeel, Danijar Hafner, and Deepak Pathak · 2020
Later among the works it cites.
Automatic curriculum learning through value disagreement
Yunzhi Zhang, Pieter Abbeel, and Lerrel Pinto · 2020
Later among the works it cites.
Deep reinforcement learning at the edge of the statistical precipice
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron Courville, and Marc G Bellemare · 2021
Closest in time.
Relative variational intrinsic control
Kate Baumli, David Warde-Farley, Steven Hansen, and Volodymyr Mnih · 2021
Closest in time.
State entropy maximization with random encoders for efficient exploration
Younggyo Seo, Lili Chen, Jinwoo Shin, Honglak Lee, Pieter Abbeel, and Kimin Lee · 2021
Closest in time.
Exploration by maximizing Rényi entropy for reward-free RL framework
Chuheng Zhang, Yuanying Cai, and Longbo Huang Jian Li · 2021
Closest in time.