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Building agents based on tree-search planning capabilities with learned models has achieved remarkable success in classic decision-making problems, such as Go and Atari.
An analysis of alpha-beta pruning
Donald E Knuth and Ronald W Moore · 1975
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Game tree searching by min/max approximation
Ronald L Rivest · 1987
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Reinforcement learning: An introduction
R. S. Sutton and A. G. Barto · 1988
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Monte-carlo planning in large pomdps
David Silver and Joel Veness · 2010
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Multi-armed bandits with episode context
Christopher D Rosin · 2011
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Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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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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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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Human-level control through deep reinforcement learning
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. A. Riedmiller, A. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, and D. Hassabis · 2015
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Deep reinforcement learning in parameterized action space
Matthew Hausknecht and Peter Stone · 2015
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Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
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Learning values across many orders of magnitude
Hado P van Hasselt, Arthur Guez, Matteo Hessel, Volodymyr Mnih, and David Silver · 2016
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Sequence-to-sequence learning as beam-search optimization
Sam Wiseman and Alexander M Rush · 2016
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Learning to play othello without human knowledge, 2016
Shantanu Thakoor, Surag Nair, and Megha Jhunjhunwala · 2016
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G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba · 2016
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An overview of gradient descent optimization algorithms
Sebastian Ruder · 2016
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 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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Mastering chess and shogi by self-play with a general reinforcement learning algorithm
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, et al · 2017
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Value-decomposition networks for cooperative multi-agent learning
Peter Sunehag, Guy Lever, Audrunas Gruslys, Wojciech Marian Czarnecki, Vinicius Zambaldi, Max Jaderberg, Marc Lanctot, Nicolas Sonnerat, Joel Z Leibo, Karl Tuyls, et al · 2017
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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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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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Distributed prioritized experience replay
Dan Horgan, John Quan, David Budden, Gabriel Barth-Maron, Matteo Hessel, Hado Van Hasselt, and David Silver · 2018
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Exploration by random network distillation
Yuri Burda, Harrison Edwards, Amos Storkey, and Oleg Klimov · 2018
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Sample-efficient reinforcement learning with stochastic ensemble value expansion
Jacob Buckman, Danijar Hafner, George Tucker, Eugene Brevdo, and Honglak Lee · 2018
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Distributional reinforcement learning with quantile regression
Will Dabney, Mark Rowland, Marc Bellemare, and Rémi Munos · 2018
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Observe and look further: Achieving consistent performance on atari
Tobias Pohlen, Bilal Piot, Todd Hester, Mohammad Gheshlaghi Azar, Dan Horgan, David Budden, Gabriel Barth-Maron, Hado Van Hasselt, John Quan, Mel Večerík, et al · 2018
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Minimalistic gridworld environment for openai gym
Maxime Chevalier-Boisvert, Lucas Willems, and Suman Pal · 2018
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Deep reinforcement learning that matters
Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, and David Meger · 2018
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JAX: composable transformations of Python+NumPy programs, 2018
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang · 2018
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Probabilistic recurrent state-space models
Andreas Doerr, Christian Daniel, Martin Schiegg, Nguyen-Tuong Duy, Stefan Schaal, Marc Toussaint, and Trimpe Sebastian · 2018
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Grandmaster level in starcraft II using multi-agent reinforcement learning
Oriol Vinyals, Igor Babuschkin, Wojciech M. Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H. Choi, Richard Powell, Timo Ewalds, Petko Georgiev, Junhyuk Oh, Dan Horgan, Manuel Kroiss, Ivo Danihelka, Aja Huang, Laurent Sifre, Trevor Cai, John P. Agapiou, Max Jaderberg, Alexander Sasha Vezhnevets, Rémi Leblond, Tobias Pohlen, Valentin Dalibard, David Budden, Yury Sulsky, James Molloy, Tom Le Paine, Çaglar Gülçehre, Ziyu Wang, Tobias Pfaff, Yuhuai Wu, Roman Ring, Dani Yogatama, Dario Wünsch, Katrina McKinney, Oliver Smith, Tom Schaul, Timothy P. Lillicrap, Koray Kavukcuoglu, Demis Hassabis, Chris Apps, and David Silver · 2019
Cited alongside, same era.
Monotonic value function factorisation for deep multi-agent reinforcement learning
Tabish Rashid, Mikayel Samvelyan, Christian Schroeder De Witt, Gregory Farquhar, Jakob Foerster, and Shimon Whiteson · 2020
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Language models are few-shot learners
Ben Mann, N Ryder, M Subbiah, J Kaplan, P Dhariwal, A Neelakantan, P Shyam, G Sastry, A Askell, S Agarwal, et al · 2020
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Mastering atari with discrete world models
Danijar Hafner, Timothy Lillicrap, Mohammad Norouzi, and Jimmy Ba · 2020
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Learning and planning in complex action spaces
Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Mohammadamin Barekatain, Simon Schmitt, and David Silver · 2021
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Planning in stochastic environments with a learned model
Ioannis Antonoglou, Julian Schrittwieser, Sherjil Ozair, Thomas K Hubert, and David Silver · 2021
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Steven Kapturowski, Georg Ostrovski, John Quan, Remi Munos, and Will Dabney · 2019
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Seed rl: Scalable and efficient deep-rl with accelerated central inference
Lasse Espeholt, Raphaël Marinier, Piotr Stanczyk, Ke Wang, and Marcin Michalski · 2019
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Dota 2 with large scale deep reinforcement learning
Christopher Berner, Greg Brockman, Brooke Chan, Vicki Cheung, Przemysław Dębiak, Christy Dennison, David Farhi, Quirin Fischer, Shariq Hashme, Chris Hesse, et al · 2019
Cited alongside, same era.
Mastering atari, go, chess and shogi by planning with a learned model
Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, Timothy P. Lillicrap, and David Silver · 2019
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
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When to trust your model: Model-based policy optimization
Michael Janner, Justin Fu, Marvin Zhang, and Sergey Levine · 2019
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Measuring the reliability of reinforcement learning algorithms
Stephanie CY Chan, Samuel Fishman, John Canny, Anoop Korattikara, and Sergio Guadarrama · 2019
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Muzero general: Open reimplementation of muzero
Aurèle Hainaut Werner Duvaud · 2019
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Mastering atari games with limited data
Weirui Ye, Shaohuai Liu, Thanard Kurutach, Pieter Abbeel, and Yang Gao · 2021
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Open-ended learning leads to generally capable agents
Open Ended Learning Team, Adam Stooke, Anuj Mahajan, Catarina Barros, Charlie Deck, Jakob Bauer, Jakub Sygnowski, Maja Trebacz, Max Jaderberg, Michael Mathieu, et al · 2021
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Online and offline reinforcement learning by planning with a learned model
Julian Schrittwieser, Thomas Hubert, Amol Mandhane, Mohammadamin Barekatain, Ioannis Antonoglou, and David Silver · 2021
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Learning and planning in complex action spaces
Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Mohammadamin Barekatain, Simon Schmitt, and David Silver · 2021
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Pettingzoo: Gym for multi-agent reinforcement learning
J Terry, Benjamin Black, Nathaniel Grammel, Mario Jayakumar, Ananth Hari, Ryan Sullivan, Luis S Santos, Clemens Dieffendahl, Caroline Horsch, Rodrigo Perez-Vicente, et al · 2021
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Facmac: Factored multi-agent centralised policy gradients
Bei Peng, Tabish Rashid, Christian Schroeder de Witt, Pierre-Alexandre Kamienny, Philip Torr, Wendelin Böhmer, and Shimon Whiteson · 2021
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DI-engine: Opendilab decision intelligence engine, 2021
OpenDILab Contributors · 2021
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Vector quantized models for planning
Sherjil Ozair, Yazhe Li, Ali Razavi, Ioannis Antonoglou, Aaron Van Den Oord, and Oriol Vinyals · 2021
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Hyar: Addressing discrete-continuous action reinforcement learning via hybrid action representation
Boyan Li, Hongyao Tang, Yan Zheng, Jianye Hao, Pengyi Li, Zhen Wang, Zhaopeng Meng, and Li Wang · 2021
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Minedojo: Building open-ended embodied agents with internet-scale knowledge
Linxi Fan, Guanzhi Wang, Yunfan Jiang, Ajay Mandlekar, Yuncong Yang, Haoyi Zhu, Andrew Tang, De-An Huang, Yuke Zhu, and Anima Anandkumar · 2022
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Policy improvement by planning with gumbel
Ivo Danihelka, Arthur Guez, Julian Schrittwieser, and David Silver · 2022
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Avalon: A benchmark for rl generalization using procedurally generated worlds
Joshua Albrecht, Abraham Fetterman, Bryden Fogelman, Ellie Kitanidis, Bartosz Wróblewski, Nicole Seo, Michael Rosenthal, Maksis Knutins, Zack Polizzi, James Simon, et al · 2022
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Raj Ghugare, Homanga Bharadhwaj, Benjamin Eysenbach, Sergey Levine, and Ruslan Salakhutdinov · 2022
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Metadrive: Composing diverse driving scenarios for generalizable reinforcement learning
Quanyi Li, Zhenghao Peng, Lan Feng, Qihang Zhang, Zhenghai Xue, and Bolei Zhou · 2022
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The surprising effectiveness of ppo in cooperative multi-agent games
Chao Yu, Akash Velu, Eugene Vinitsky, Jiaxuan Gao, Yu Wang, Alexandre Bayen, and Yi Wu · 2022
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Planning for sample efficient imitation learning
Zhao-Heng Yin, Weirui Ye, Qifeng Chen, and Yang Gao · 2022
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Monte carlo tree search: A review of recent modifications and applications
Maciej Świechowski, Konrad Godlewski, Bartosz Sawicki, and Jacek Mańdziuk · 2023
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Gobigger: A scalable platform for cooperative-competitive multi-agent interactive simulation
Ming Zhang, Shenghan Zhang, Zhenjie Yang, Lekai Chen, Jinliang Zheng, Chao Yang, Chuming Li, Hang Zhou, Yazhe Niu, and Yu Liu · 2023
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Speedyzero: Mastering atari with limited data and time
Yixuan Mei, Jiaxuan Gao, Weirui Ye, Shaohuai Liu, Yang Gao, and Yi Wu · 2023
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Efficient offline policy optimization with a learned model
Zichen Liu, Siyi Li, Wee Sun Lee, Shuicheng Yan, and Zhongwen Xu · 2023
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Pytorch 2.0: The journey to bringing compiler technologies to the core of pytorch (keynote)
Peng Wu · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
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