“Are AlphaZero-like Agents Robust to Adversarial Perturbations?”, Lan Et Al 2022

“Are AlphaZero-like Agents Robust to Adversarial Perturbations?”⁠, Li-Cheng Lan, Huan Zhang, Ti-Rong Wu, Meng-Yu Tsai, I-Chen Wu, Cho-Jui Hsieh2022-11-07 (neural net/adversarial; similar; bibliography)

“Large-Scale Retrieval for Reinforcement Learning”, Humphreys Et Al 2022

“Large-Scale Retrieval for Reinforcement Learning”⁠, Peter C. Humphreys, Arthur Guez, Olivier Tieleman, Laurent Sifre, Théophane Weber, Timothy Lillicrap2022-06-10 (retrieval AI⁠, MuZero; similar; bibliography)

“HTPS: HyperTree Proof Search for Neural Theorem Proving”, Lample Et Al 2022

“HTPS: HyperTree Proof Search for Neural Theorem Proving”⁠, Guillaume Lample, Marie-Anne Lachaux, Thibaut Lavril, Xavier Martinet, Amaury Hayat, Gabriel Ebner, Aurélien Rodriguez2022-05-23 (Transformer NN⁠, math; similar)

“CrossBeam: Learning to Search in Bottom-Up Program Synthesis”, Shi Et Al 2022

“CrossBeam: Learning to Search in Bottom-Up Program Synthesis”⁠, Kensen Shi, Hanjun Dai, Kevin Ellis, Charles Sutton2022-03-20 (similar)

“Policy Improvement by Planning With Gumbel”, Danihelka Et Al 2022

“Policy improvement by planning with Gumbel”⁠, Ivo Danihelka, Arthur Guez, Julian Schrittwieser, David Silver2022-03-04 (RL exploration⁠, MuZero; similar; bibliography)

“Player of Games”, Schmid Et Al 2021

“Player of Games”⁠, Martin Schmid, Matej Moravcik, Neil Burch, Rudolf Kadlec, Josh Davidson, Kevin Waugh, Nolan Bard, Finbarr Timbers2021-12-06 (poker AI⁠, MARL; similar; bibliography)

“Ν-SDDP: Neural Stochastic Dual Dynamic Programming”, Dai Et Al 2021

“ν-SDDP: Neural Stochastic Dual Dynamic Programming”⁠, Hanjun Dai, Yuan Xue, Zia Syed, Dale Schuurmans, Bo Dai2021-12-01 (decision theory; similar)

“Acquisition of Chess Knowledge in AlphaZero”, McGrath Et Al 2021

“Acquisition of Chess Knowledge in AlphaZero”⁠, Thomas McGrath, Andrei Kapishnikov, Nenad Tomašev, Adam Pearce, Demis Hassabis, Been Kim, Ulrich Paquet2021-11-17 (AI chess⁠, RL scaling; similar; bibliography)

“Evaluating Model-based Planning and Planner Amortization for Continuous Control”, Byravan Et Al 2021

“Evaluating model-based planning and planner amortization for continuous control”⁠, Arunkumar Byravan, Leonard Hasenclever, Piotr Trochim, Mehdi Mirza, Alessandro Davide Ialongo, Yuval Tassa2021-10-07 (similar)

“Scalable Online Planning via Reinforcement Learning Fine-Tuning”, Fickinger Et Al 2021

“Scalable Online Planning via Reinforcement Learning Fine-Tuning”⁠, Arnaud Fickinger, Hengyuan Hu, Brandon Amos, Stuart Russell, Noam Brown2021-09-30 (Hanabi AI⁠, meta-learning; similar)

“Lessons from AlphaZero for Optimal, Model Predictive, and Adaptive Control”, Bertsekas2021

“Lessons from AlphaZero for Optimal, Model Predictive, and Adaptive Control”⁠, Dimitri Bertsekas2021-08-20 (similar)

“How Does AI Improve Human Decision-Making? Evidence from the AI-Powered Go Program”, Choi Et Al 2021

“How Does AI Improve Human Decision-Making? Evidence from the AI-Powered Go Program”⁠, Sukwoong Choi, Namil Kim, Junsik Kim, Hyo Kang2021-07-26 (chess psychology; backlinks; similar)

“Train on Small, Play the Large: Scaling Up Board Games With AlphaZero and GNN”, Ben-Assayag & El-Yaniv2021

“Train on Small, Play the Large: Scaling Up Board Games with AlphaZero and GNN”⁠, Shai Ben-Assayag, Ran El-Yaniv2021-07-18 (similar)

“Neural Tree Expansion for Multi-Robot Planning in Non-Cooperative Environments”, Riviere Et Al 2021

“Neural Tree Expansion for Multi-Robot Planning in Non-Cooperative Environments”⁠, Benjamin Riviere, Wolfgang Hoenig, Matthew Anderson, Soon-Jo Chung2021-04-20 (MARL⁠, robotics; similar)

“Scaling Scaling Laws With Board Games”, Jones2021

“Scaling Scaling Laws with Board Games”⁠, Andy L. Jones2021-04-07 (CS/hardware⁠, MuZero⁠, RL scaling; backlinks; similar)

“OLIVAW: Mastering Othello without Human Knowledge, nor a Fortune”, Norelli & Panconesi2021

“OLIVAW: Mastering Othello without Human Knowledge, nor a Fortune”⁠, Antonio Norelli, Alessandro Panconesi2021-03-31 (similar)

“Transfer of Fully Convolutional Policy-Value Networks Between Games and Game Variants”, Soemers Et Al 2021

“Transfer of Fully Convolutional Policy-Value Networks Between Games and Game Variants”⁠, Dennis J. N. J. Soemers, Vegard Mella, Eric Piette, Matthew Stephenson, Cameron Browne, Olivier Teytaud2021-02-24 (CNN; similar)

“Investment vs. Reward in a Competitive Knapsack Problem”, Neumann & Gros2021

“Investment vs. reward in a competitive knapsack problem”⁠, Oren Neumann, Claudius Gros2021-01-26 (CS/algorithm⁠, RL scaling; similar)

“Learning to Stop: Dynamic Simulation Monte-Carlo Tree Search”, Lan Et Al 2020

“Learning to Stop: Dynamic Simulation Monte-Carlo Tree Search”⁠, Li-Cheng Lan, Meng-Yu Tsai, Ti-Rong Wu, I-Chen Wu, Cho-Jui Hsieh2020-12-14 (similar)

“Assessing Game Balance With AlphaZero: Exploring Alternative Rule Sets in Chess”, Tomašev Et Al 2020

“Assessing Game Balance with AlphaZero: Exploring Alternative Rule Sets in Chess”⁠, Nenad Tomašev, Ulrich Paquet, Demis Hassabis, Vladimir Kramnik2020-09-09 (AI chess⁠, RL exploration; similar)

“Learning Personalized Models of Human Behavior in Chess”, McIlroy-Young Et Al 2020

“Learning Personalized Models of Human Behavior in Chess”⁠, Reid McIlroy-Young, Russell Wang, Siddhartha Sen, Jon Kleinberg, Ashton Anderson2020-08-23 (chess psychology⁠, AI chess⁠, preference learning⁠, stylometry; similar)

“ReBeL: Combining Deep Reinforcement Learning and Search for Imperfect-Information Games”, Brown Et Al 2020

“ReBeL: Combining Deep Reinforcement Learning and Search for Imperfect-Information Games”⁠, Noam Brown, Anton Bakhtin, Adam Lerer, Qucheng Gong2020-07-27 (poker AI⁠, MARL; similar)

“Learning Compositional Neural Programs for Continuous Control”, Pierrot Et Al 2020

“Learning Compositional Neural Programs for Continuous Control”⁠, Thomas Pierrot, Nicolas Perrin, Feryal Behbahani, Alexandre Laterre, Olivier Sigaud, Karim Beguir, Nando de Freitas2020-07-27 (similar)

“Monte-Carlo Tree Search As Regularized Policy Optimization”, Grill Et Al 2020

“Monte-Carlo Tree Search as Regularized Policy Optimization”⁠, Jean-Bastien Grill, Florent Altché, Yunhao Tang, Thomas Hubert, Michal Valko, Ioannis Antonoglou, Rémi Munos2020-07-24 (MuZero; similar)

“Tackling Morpion Solitaire With AlphaZero-like Ranked Reward Reinforcement Learning”, Wang Et Al 2020

“Tackling Morpion Solitaire with AlphaZero-like Ranked Reward Reinforcement Learning”⁠, Hui Wang, Mike Preuss, Michael Emmerich, Aske Plaat2020-06-14 (similar)

“Aligning Superhuman AI With Human Behavior: Chess As a Model System”, McIlroy-Young Et Al 2020

“Aligning Superhuman AI with Human Behavior: Chess as a Model System”⁠, Reid McIlroy-Young, Siddhartha Sen, Jon Kleinberg, Ashton Anderson2020-06-02 (chess psychology⁠, AI chess⁠, preference learning; similar)

“Neural Machine Translation With Monte-Carlo Tree Search”, Parker & Chen2020

“Neural Machine Translation with Monte-Carlo Tree Search”⁠, Jerrod Parker, Jerry Zikun Chen2020-04-27 (similar)

“Approximate Exploitability: Learning a Best Response in Large Games”, Timbers Et Al 2020

“Approximate exploitability: Learning a best response in large games”⁠, Finbarr Timbers, Edward Lockhart, Marc Lanctot, Martin Schmid, Julian Schrittwieser, Thomas Hubert, Michael Bowling2020-04-20 (RL exploration⁠, poker AI⁠, meta-learning⁠, MARL; similar)

“Real World Games Look Like Spinning Tops”, Czarnecki Et Al 2020

“Real World Games Look Like Spinning Tops”⁠, Wojciech Marian Czarnecki, Gauthier Gidel, Brendan Tracey, Karl Tuyls, Shayegan Omidshafiei, David Balduzzi2020-04-20 (RL exploration⁠, MARL; similar)

“Accelerating and Improving AlphaZero Using Population Based Training”, Wu Et Al 2020

“Accelerating and Improving AlphaZero Using Population Based Training”⁠, Ti-Rong Wu, Ting-Han Wei, I-Chen Wu2020-03-13 (meta-learning; similar)

“Self-Play Learning Without a Reward Metric”, Schmidt Et Al 2019

“Self-Play Learning Without a Reward Metric”⁠, Dan Schmidt, Nick Moran, Jonathan S. Rosenfeld, Jonathan Rosenthal, Jonathan Yedidia2019-12-16 (order statistics/comparison; backlinks; similar)

“(Yonhap Interview) Go Master Lee Says He Quits Unable to Win over AI Go Players”, Agency2019

“(Yonhap Interview) Go master Lee says he quits unable to win over AI Go players”⁠, Yonhap News Agency2019-11-27 (similar)

“MuZero: Mastering Atari, Go, Chess and Shogi by Planning With a Learned Model”, Schrittwieser Et Al 2019

“MuZero: Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model”⁠, Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt2019-11-19 (RNN⁠, AI chess⁠, MuZero; similar)

“Multiplayer AlphaZero”, Petosa & Balch2019

“Multiplayer AlphaZero”⁠, Nick Petosa, Tucker Balch2019-10-29 (similar)

“Global Optimization of Quantum Dynamics With AlphaZero Deep Exploration”, Dalgaard Et Al 2019

“Global optimization of quantum dynamics with AlphaZero deep exploration”⁠, Mogens Dalgaard, Felix Motzoi, Jens Jakob Sorensen, Jacob Sherson2019-07-12 (similar)

“Learning Compositional Neural Programs With Recursive Tree Search and Planning”, Pierrot Et Al 2019

“Learning Compositional Neural Programs with Recursive Tree Search and Planning”⁠, Thomas Pierrot, Guillaume Ligner, Scott Reed, Olivier Sigaud, Nicolas Perrin, Alexandre Laterre, David Kas2019-05-30 (similar)

“Π-IW: Deep Policies for Width-Based Planning in Pixel Domains”, Junyent Et Al 2019

“π-IW: Deep Policies for Width-Based Planning in Pixel Domains”⁠, Miquel Junyent, Anders Jonsson, Vicenç Gómez2019-04-12 (RL exploration; similar)

“Policy Gradient Search: Online Planning and Expert Iteration without Search Trees”, Anthony Et Al 2019

“Policy Gradient Search: Online Planning and Expert Iteration without Search Trees”⁠, Thomas Anthony, Robert Nishihara, Philipp Moritz, Tim Salimans, John Schulman2019-04-07 (similar)

“AlphaX: EXploring Neural Architectures With Deep Neural Networks and Monte Carlo Tree Search”, Wang Et Al 2019

“AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Search”⁠, Linnan Wang, Yiyang Zhao, Yuu Jinnai, Yuandong Tian, Rodrigo Fonseca2019-03-26 (meta-learning; similar)

“Minigo: A Case Study in Reproducing Reinforcement Learning Research”, Anonymous2019

“Minigo: A Case Study in Reproducing Reinforcement Learning Research”⁠, Anonymous2019-03-06 (similar)

“Α-Rank: Multi-Agent Evaluation by Evolution”, Omidshafiei Et Al 2019

“α-Rank: Multi-Agent Evaluation by Evolution”⁠, Shayegan Omidshafiei, Christos Papadimitriou, Georgios Piliouras, Karl Tuyls, Mark Rowland, Jean-Baptiste Lespiau2019-03-04 (poker AI⁠, MARL; similar)

“Accelerating Self-Play Learning in Go”, Wu2019

“Accelerating Self-Play Learning in Go”⁠, David J. Wu2019-02-27 (experience curves; similar)

“ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero”, Tian Et Al 2019

“ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero”⁠, Yuandong Tian, Jerry Ma, Qucheng Gong, Shubho Sengupta, Zhuoyuan Chen, James Pinkerton, C. Lawrence Zitnick2019-02-12 (similar)

“Bayesian Optimization in AlphaGo”, Chen Et Al 2018

“Bayesian Optimization in AlphaGo”⁠, Yutian Chen, Aja Huang, Ziyu Wang, Ioannis Antonoglou, Julian Schrittwieser, David Silver, Nando de Freitas2018-12-17 (similar)

“A General Reinforcement Learning Algorithm That Masters Chess, Shogi, and Go through Self-play”, Silver Et Al 2018

“A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play”⁠, David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot2018-12-07 (AI chess; similar)

“Deep Reinforcement Learning”, Li2018

“Deep Reinforcement Learning”⁠, Yuxi Li2018-10-15 (meta-learning⁠, MARL⁠, robotics; similar)

“AlphaSeq: Sequence Discovery With Deep Reinforcement Learning”, Shao Et Al 2018

“AlphaSeq: Sequence Discovery with Deep Reinforcement Learning”⁠, Yulin Shao, Soung Chang Liew, Taotao Wang2018-09-26 (similar)

“ExIt-OOS: Towards Learning from Planning in Imperfect Information Games”, Kitchen & Benedetti2018

“ExIt-OOS: Towards Learning from Planning in Imperfect Information Games”⁠, Andy Kitchen, Michela Benedetti2018-08-30 (RL/imperfect-information; similar)

“Has Dynamic Programming Improved Decision Making?”, Rust2018

“Has dynamic programming improved decision making?”⁠, John Rust2018-08-22 (backlinks; similar)

“Improving Width-based Planning With Compact Policies”, Junyent Et Al 2018

“Improving width-based planning with compact policies”⁠, Miquel Junyent, Anders Jonsson, Vicenç Gómez2018-06-15 (RL exploration; similar)

“Surprising Negative Results for Generative Adversarial Tree Search”, Azizzadenesheli Et Al 2018

“Surprising Negative Results for Generative Adversarial Tree Search”⁠, Kamyar Azizzadenesheli, Brandon Yang, Weitang Liu, Zachary C. Lipton, Animashree Anandkumar2018-06-15 (MuZero; similar)

“Dual Policy Iteration”, Sun Et Al 2018

“Dual Policy Iteration”⁠, Wen Sun, Geoffrey J. Gordon, Byron Boots, J. Andrew Bagnell2018-05-28 (similar)

“Solving the Rubik’s Cube Without Human Knowledge”, McAleer Et Al 2018

“Solving the Rubik’s Cube Without Human Knowledge”⁠, Stephen McAleer, Forest Agostinelli, Alexander Shmakov, Pierre Baldi2018-05-18 (similar)

“Feedback-Based Tree Search for Reinforcement Learning”, Jiang Et Al 2018

“Feedback-Based Tree Search for Reinforcement Learning”⁠, Daniel R. Jiang, Emmanuel Ekwedike, Han Liu2018-05-15 (similar)

“A Tree Search Algorithm for Sequence Labeling”, Lao Et Al 2018

“A Tree Search Algorithm for Sequence Labeling”⁠, Yadi Lao, Jun Xu, Yanyan Lan, Jiafeng Guo, Sheng Gao, Xueqi Cheng2018-04-29 (RNN; similar)

“Feature-Based Aggregation and Deep Reinforcement Learning: A Survey and Some New Implementations”, Bertsekas2018

“Feature-Based Aggregation and Deep Reinforcement Learning: A Survey and Some New Implementations”⁠, Dimitri P. Bertsekas2018-04-12 (tech; backlinks; similar)

“Sim-to-Real Optimization of Complex Real World Mobile Network With Imperfect Information via Deep Reinforcement Learning from Self-play”, Tan Et Al 2018

“Sim-to-Real Optimization of Complex Real World Mobile Network with Imperfect Information via Deep Reinforcement Learning from Self-play”⁠, Yongxi Tan, Jin Yang, Xin Chen, Qitao Song, Yunjun Chen, Zhangxiang Ye, Zhenqiang Su2018-02-18 (CNN⁠, MARL; similar)

“Learning to Search With MCTSnets”, Guez Et Al 2018

“Learning to Search with MCTSnets”⁠, Arthur Guez, Théophane Weber, Ioannis Antonoglou, Karen Simonyan, Oriol Vinyals, Daan Wierstra, Rémi Munos2018-02-13 (RL exploration; similar)

“M-Walk: Learning to Walk over Graphs Using Monte Carlo Tree Search”, Shen Et Al 2018

“M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search”⁠, Yelong Shen, Jianshu Chen, Po-Sen Huang, Yuqing Guo, Jianfeng Gao2018-02-12 (RNN; similar)

“Mastering Chess and Shogi by Self-Play With a General Reinforcement Learning Algorithm”, Silver Et Al 2017

“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 Lanctot2017-12-05 (math; similar)

“AlphaGo Zero: Mastering the Game of Go without Human Knowledge”, Silver Et Al 2017

“AlphaGo Zero: Mastering the game of Go without human knowledge”⁠, David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert2017-10-19 (neural net; similar; bibliography)

“DeepMind’s Latest AI Breakthrough Is Its Most Important Yet: Google-owned DeepMind’s Go-playing Artificial Intelligence Can Now Learn without Human Help… or Data”, Burgess2017

“DeepMind’s latest AI breakthrough is its most important yet: Google-owned DeepMind’s Go-playing artificial intelligence can now learn without human help… or data”⁠, Matt Burgess2017-10-18 (similar)

“Self-taught AI Is Best yet at Strategy Game Go”, Gibney2017

“Self-taught AI is best yet at strategy game Go”⁠, Elizabeth Gibney2017-10-18

“Learning Generalized Reactive Policies Using Deep Neural Networks”, Groshev Et Al 2017

“Learning Generalized Reactive Policies using Deep Neural Networks”⁠, Edward Groshev, Maxwell Goldstein, Aviv Tamar, Siddharth Srivastava, Pieter Abbeel2017-08-24 (similar)

“Learning to Plan Chemical Syntheses”, Segler Et Al 2017

“Learning to Plan Chemical Syntheses”⁠, Marwin H. S. Segler, Mike Preuss, Mark P. Waller2017-08-14 (science; similar)

“Thinking Fast and Slow With Deep Learning and Tree Search”, Anthony Et Al 2017

“Thinking Fast and Slow with Deep Learning and Tree Search”⁠, Thomas Anthony, Zheng Tian, David Barber2017-05-23 (similar)

“DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker”, Moravčík Et Al 2017

“DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker”⁠, Matej Moravčík, Martin Schmid, Neil Burch, Viliam Lisý, Dustin Morrill, Nolan Bard, Trevor Davis, Kevin Waugh2017-01-06 (poker AI; backlinks; similar)

“Mastering the Game of Go With Deep Neural Networks and Tree Search”, Silver Et Al 2016

“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 Schrittwieser2016-01-28 (neural net; similar)

“Giraffe: Using Deep Reinforcement Learning to Play Chess”, Lai2015

“Giraffe: Using Deep Reinforcement Learning to Play Chess”⁠, Matthew Lai2015-09-04 (AI chess; similar)

“Algorithmic Progress in Six Domains”, Grace2013

“Algorithmic Progress in Six Domains”⁠, Katja Grace2013-12-09 (AI scaling⁠, CS/algorithm⁠, CS/hardware⁠, experience curves⁠, AI chess⁠, RL scaling; similar)

“Reinforcement Learning As Classification: Leveraging Modern Classifiers”, Lagoudakis & Parr2003

Wikipedia

1. AlphaGo

2. AlphaZero

3. Darkforest

4. David Silver (programmer)

5. Demis Hassabis

6. Fan Hui

7. Fine Art (software)

8. Lee Sedol

9. Master (software)

10. Monte Carlo tree search

11. Tensor processing unit