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Exploration bonuses in reinforcement learning guide long-horizon exploration by defining custom intrinsic objectives.
Using confidence bounds for exploitation-exploration trade-offs
Peter Auer · 2002
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An intrinsic reward mechanism for efficient exploration
Özgür Şimşek and Andrew G Barto · 2006
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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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Near-bayesian exploration in polynomial time
J Zico Kolter and Andrew Y Ng · 2009
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Intrinsically motivated reinforcement learning: An evolutionary perspective
Satinder Singh, Richard L Lewis, Andrew G Barto, and Jonathan Sorg · 2010
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Continuous control with deep reinforcement learning
Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2015
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Unifying count-based exploration and intrinsic motivation
Marc Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, and Remi Munos · 2016
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Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 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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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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# exploration: A study of count-based exploration for deep reinforcement learning
Haoran Tang, Rein Houthooft, Davis Foote, Adam Stooke, OpenAI Xi Chen, Yan Duan, John Schulman, Filip DeTurck, and Pieter Abbeel · 2017
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Exploration by random network distillation
Yuri Burda, Harrison Edwards, Amos Storkey, and Oleg Klimov · 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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Diversity is all you need: Learning skills without a reward function
Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, and Sergey Levine · 2018
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Addressing function approximation error in actor-critic methods
Scott Fujimoto, Herke Hoof, and David Meger · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
Cited alongside, same era.
On evaluating the generalization of lstm models in formal languages
Mirac Suzgun, Yonatan Belinkov, and Stuart M Shieber · 2018
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Smirl: Surprise minimizing reinforcement learning in unstable environments
Glen Berseth, Daniel Geng, Coline Devin, Nicholas Rhinehart, Chelsea Finn, Dinesh Jayaraman, and Sergey Levine · 2019
Cited alongside, same era.
Non-stationary markov decision processes, a worst-case approach using model-based reinforcement learning
Erwan Lecarpentier and Emmanuel Rachelson · 2019
Cited alongside, same era.
Efficient exploration via state marginal matching
Lisa Lee, Benjamin Eysenbach, Emilio Parisotto, Eric Xing, Sergey Levine, and Ruslan Salakhutdinov · 2019
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
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Recurrent model-free rl can be a strong baseline for many pomdps
Tianwei Ni, Benjamin Eysenbach, and Ruslan Salakhutdinov · 2021
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Stable-baselines3: Reliable reinforcement learning implementations
Antonin Raffin, Ashley Hill, Adam Gleave, Anssi Kanervisto, Maximilian Ernestus, and Noah Dormann · 2021
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Habitat-matterport 3d dataset (HM3d): 1000 large-scale 3d environments for embodied AI
Santhosh Kumar Ramakrishnan, Aaron Gokaslan, Erik Wijmans, Oleksandr Maksymets, Alexander Clegg, John M Turner, Eric Undersander, Wojciech Galuba, Andrew Westbury, Angel X Chang, Manolis Savva, Yili Zhao, and Dhruv Batra · 2021
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Cited alongside, same era.
Behaviour suite for reinforcement learning
Ian Osband, Yotam Doron, Matteo Hessel, John Aslanides, Eren Sezener, Andre Saraiva, Katrina McKinney, Tor Lattimore, Csaba Szepesvari, Satinder Singh, et al · 2019
Cited alongside, same era.
Dealing with non-stationarity in multi-agent deep reinforcement learning
Georgios Papoudakis, Filippos Christianos, Arrasy Rahman, and Stefano V Albrecht · 2019
Cited alongside, same era.
Habitat: A Platform for Embodied AI Research
Manolis Savva, Abhishek Kadian, Oleksandr Maksymets, Yili Zhao, Erik Wijmans, Bhavana Jain, Julian Straub, Jia Liu, Vladlen Koltun, Jitendra Malik, Devi Parikh, and Dhruv Batra · 2019
Cited alongside, same era.
Deep reinforcement learning for navigation in aaa video games
Eloi Alonso, Maxim Peter, David Goumard, and Joshua Romoff · 2020
Cited alongside, same era.
Reinforcement learning for non-stationary markov decision processes: The blessing of (more) optimism
Wang Chi Cheung, David Simchi-Levi, and Ruihao Zhu · 2020
Cited alongside, same era.
Leveraging procedural generation to benchmark reinforcement learning
Karl Cobbe, Chris Hesse, Jacob Hilton, and John Schulman · 2020
Cited alongside, same era.
Offline reinforcement learning: Tutorial, review, and perspectives on open problems
Sergey Levine, Aviral Kumar, George Tucker, and Justin Fu · 2020
Cited alongside, same era.
Mikayel Samvelyan, Robert Kirk, Vitaly Kurin, Jack Parker-Holder, Minqi Jiang, Eric Hambro, Fabio Petroni, Heinrich Küttler, Edward Grefenstette, and Tim Rocktäschel · 2021
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Decoupled reinforcement learning to stabilise intrinsically-motivated exploration
Lukas Schäfer, Filippos Christianos, Josiah P Hanna, and Stefano V Albrecht · 2021
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State entropy maximization with random encoders for efficient exploration
Younggyo Seo, Lili Chen, Jinwoo Shin, Honglak Lee, Pieter Abbeel, and Kimin Lee · 2021
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Habitat 2.0: Training home assistants to rearrange their habitat
Andrew Szot, Alex Clegg, Eric Undersander, Erik Wijmans, Yili Zhao, John Turner, Noah Maestre, Mustafa Mukadam, Devendra Chaplot, Oleksandr Maksymets, Aaron Gokaslan, Vladimir Vondrus, Sameer Dharur, Franziska Meier, Wojciech Galuba, Angel Chang, Zsolt Kira, Vladlen Koltun, Jitendra Malik, Manolis Savva, and Dhruv Batra · 2021
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Decoupled exploration and exploitation policies for sample-efficient reinforcement learning
William F Whitney, Michael Bloesch, Jost Tobias Springenberg, Abbas Abdolmaleki, Kyunghyun Cho, and Martin Riedmiller · 2021
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Reinforcement learning with prototypical representations
Denis Yarats, Rob Fergus, Alessandro Lazaric, and Lerrel Pinto · 2021
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Exploration via elliptical episodic bonuses
Mikael Henaff, Roberta Raileanu, Minqi Jiang, and Tim Rocktäschel · 2022
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Revisiting intrinsic reward for exploration in procedurally generated environments
Kaixin Wang, Kuangqi Zhou, Bingyi Kang, Jiashi Feng, and YAN Shuicheng · 2022
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A survey of meta-reinforcement learning
Jacob Beck, Risto Vuorio, Evan Zheran Liu, Zheng Xiong, Luisa Zintgraf, Chelsea Finn, and Shimon Whiteson · 2023
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A study of global and episodic bonuses for exploration in contextual mdps
Mikael Henaff, Minqi Jiang, and Roberta Raileanu · 2023
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Flipping coins to estimate pseudocounts for exploration in reinforcement learning
Sam Lobel, Akhil Bagaria, and George Konidaris · 2023
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Approximate information state based convergence analysis of recurrent q-learning
Erfan Seyedsalehi, Nima Akbarzadeh, Amit Sinha, and Aditya Mahajan · 2023
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Explore to generalize in zero-shot rl
Ev Zisselman, Itai Lavie, Daniel Soudry, and Aviv Tamar · 2023
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Maximum state entropy exploration using predecessor and successor representations
Arnav Kumar Jain, Lucas Lehnert, Irina Rish, and Glen Berseth · 2024
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