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Extrinsic rewards can effectively guide reinforcement learning (RL) agents in specific tasks.
A markovian decision process
Richard Bellman · 1957
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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An analysis of model-based interval estimation for markov decision processes
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A contextual-bandit approach to personalized news article recommendation
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The arcade learning environment: An evaluation platform for general agents
Marc G Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling · 2013
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Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
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Incentivizing exploration in reinforcement learning with deep predictive models
Bradly C Stadie, Sergey Levine, and Pieter Abbeel · 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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Count-based exploration in feature space for reinforcement learning
Jarryd Martin, Suraj Narayanan Sasikumar, Tom Everitt, and Marcus Hutter · 2017
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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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Bebold: Exploration beyond the boundary of explored regions
Tianjun Zhang, Huazhe Xu, Xiaolong Wang, Yi Wu, Kurt Keutzer, Joseph E Gonzalez, and Yuandong Tian · 2017
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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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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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Recurrent experience replay in distributed reinforcement learning
Steven Kapturowski, Georg Ostrovski, John Quan, Remi Munos, and Will Dabney · 2018
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Super Mario Bros for OpenAI Gym
Christian Kauten · 2018
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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Self-supervised exploration via disagreement
Deepak Pathak, Dhiraj Gandhi, and Abhinav Gupta · 2019
On bonus-based exploration methods in the arcade learning environment
Adrien Ali Taiga, William Fedus, Marlos C Machado, Aaron Courville, and Marc G Bellemare · 2021
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Byol-explore: Exploration by bootstrapped prediction
Zhaohan Guo, Shantanu Thakoor, Miruna Pîslar, Bernardo Avila Pires, Florent Altché, Corentin Tallec, Alaa Saade, Daniele Calandriello, Jean-Bastien Grill, Yunhao Tang, et al · 2022
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Exploration via elliptical episodic bonuses
Mikael Henaff, Roberta Raileanu, Minqi Jiang, and Tim Rocktäschel · 2022
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Leco: Learnable episodic count for task-specific intrinsic reward
Daejin Jo, Sungwoong Kim, Daniel Nam, Taehwan Kwon, Seungeun Rho, Jongmin Kim, and Donghoon Lee · 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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Episodic curiosity through reachability
Nikolay Savinov, Anton Raichuk, Damien Vincent, Raphael Marinier, Marc Pollefeys, Timothy Lillicrap, and Sylvain Gelly · 2019
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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, and Charles Blundell · 2020
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Leveraging procedural generation to benchmark reinforcement learning
Karl Cobbe, Chris Hesse, Jacob Hilton, and John Schulman · 2020
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Implementation matters in deep policy gradients: A case study on ppo and trpo
Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph, and Aleksander Madry · 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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Ride: Rewarding impact-driven exploration for procedurally-generated environments
Roberta Raileanu and Tim Rocktäschel · 2020
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An information-theoretic perspective on intrinsic motivation in reinforcement learning: A survey
Arthur Aubret, Laetitia Matignon, and Salima Hassas · 2023
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Improving intrinsic exploration by creating stationary objectives
Roger Creus Castanyer, Joshua Romoff, and Glen Berseth · 2023
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Minigrid & miniworld: Modular & customizable reinforcement learning environments for goal-oriented tasks
Maxime Chevalier-Boisvert, Bolun Dai, Mark Towers, Rodrigo Perez-Vicente, Lucas Willems, Salem Lahlou, Suman Pal, Pablo Samuel Castro, and Jordan Terry · 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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The 37 implementation details of proximal policy optimization
Shengyi Huang, Rousslan Fernand Julien Dossa, Antonin Raffin, Anssi Kanervisto, and Weixun Wang · 2023
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General intelligence requires rethinking exploration
Minqi Jiang, Tim Rocktäschel, and Edward Grefenstette · 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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Gymnasium, March 2023
Mark Towers, Jordan K. Terry, Ariel Kwiatkowski, John U. Balis, Gianluca de Cola, Tristan Deleu, Manuel Goulão, Andreas Kallinteris, Arjun KG, Markus Krimmel, Rodrigo Perez-Vicente, Andrea Pierré, Sander Schulhoff, Jun Jet Tai, Andrew Tan Jin Shen, and Omar G. Younis · 2023
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Rllte: Long-term evolution project of reinforcement learning
Mingqi Yuan, Zequn Zhang, Yang Xu, Shihao Luo, Bo Li, Xin Jin, and Wenjun Zeng · 2023
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Gymnasium robotics, 2024
Rodrigo de Lazcano, Kallinteris Andreas, Jun Jet Tai, Seungjae Ryan Lee, and Jordan Terry · 2024
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Craftax: A lightning-fast benchmark for open-ended reinforcement learning
Michael Matthews, Michael Beukman, Benjamin Ellis, Mikayel Samvelyan, Matthew Jackson, Samuel Coward, and Jakob Foerster · 2024
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Can we hop in general? a discussion of benchmark selection and design using the hopper environment
Claas A Voelcker, Marcel Hussing, and Eric Eaton · 2024
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