“Melting Pot 2.0”, Agapiou Et Al 2022

“Melting Pot 2.0”⁠, John P. Agapiou, Alexander Sasha Vezhnevets, Edgar A. Duéñez-Guzmán, Jayd Matyas, Yiran Mao, Peter Sunehag2022-11-24 (RNN⁠, model-free RL; similar)

“CICERO: Human-level Play in the Game Of Diplomacy By Combining Language Models With Strategic Reasoning”, Team Et Al 2022

“CICERO: Human-level play in the game of Diplomacy by combining language models with strategic reasoning”⁠, Meta Fundamental AI Research Diplomacy Team, Anton Bakhtin, Noam Brown, Emily Dinan, Gabriele Farina2022-11-22 (Diplomacy AI⁠, model-based RL; similar; bibliography)

“Over-communicate No More: Situated RL Agents Learn Concise Communication Protocols”, Kalinowska Et Al 2022

“Over-communicate no more: Situated RL agents learn concise communication protocols”⁠, Aleksandra Kalinowska, Elnaz Davoodi, Florian Strub, Kory W. Mathewson, Ivana Kajic, Michael Bowling2022-11-02 (model-free RL; similar)

“Human-AI Coordination via Human-Regularized Search and Learning”, Hu Et Al 2022

“Human-AI Coordination via Human-Regularized Search and Learning”⁠, Hengyuan Hu, David J. Wu, Adam Lerer, Jakob Foerster, Noam Brown2022-10-11 (Hanabi AI; similar)

“Game Theoretic Rating in N-player General-sum Games With Equilibria”, Marris Et Al 2022

“Game Theoretic Rating in N-player general-sum games with Equilibria”⁠, Luke Marris, Marc Lanctot, Ian Gemp, Shayegan Omidshafiei, Stephen McAleer, Jerome Connor, Karl Tuyls2022-10-05 (similar)

“Modeling Bounded Rationality in Multi-Agent Simulations Using Rationally Inattentive Reinforcement Learning”, Anonymous2022

“Modeling Bounded Rationality in Multi-Agent Simulations Using Rationally Inattentive Reinforcement Learning”⁠, Anonymous2022-09-19 (economics⁠, decision theory; similar; bibliography)

“Neural Payoff Machines: Predicting Fair and Stable Payoff Allocations Among Team Members”, Cornelisse Et Al 2022

“Neural Payoff Machines: Predicting Fair and Stable Payoff Allocations Among Team Members”⁠, Daphne Cornelisse, Thomas Rood, Mateusz Malinowski, Yoram Bachrach, Tal Kachman2022-08-18 (knowledge distillation⁠, economics; similar)

“Social Simulacra: Creating Populated Prototypes for Social Computing Systems”, Park Et Al 2022

“Social Simulacra: Creating Populated Prototypes for Social Computing Systems”⁠, Joon Sung Park, Lindsay Popowski, Carrie J. Cai, Meredith Ringel Morris, Percy Liang, Michael S. Bernstein2022-08-08 (GPT fiction⁠, text game; backlinks; similar; bibliography)

“DeepNash: Mastering the Game of Stratego With Model-Free Multiagent Reinforcement Learning”, Perolat Et Al 2022

“DeepNash: Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning”⁠, Julien Perolat, Bart de Vylder, Daniel Hennes, Eugene Tarassov, Florian Strub, Vincent de Boer, Paul Muller2022-06-30 (RL/imperfect-information; similar; bibliography)

“Fleet-DAgger: Interactive Robot Fleet Learning With Scalable Human Supervision”, Hoque Et Al 2022

“Fleet-DAgger: Interactive Robot Fleet Learning with Scalable Human Supervision”⁠, Ryan Hoque, Lawrence Yunliang Chen, Satvik Sharma, Karthik Dharmarajan, Brijen Thananjeyan, Pieter Abbeel2022-06-29 (robotics; similar)

“Revisiting Some Common Practices in Cooperative Multi-Agent Reinforcement Learning”, Fu Et Al 2022

“Revisiting Some Common Practices in Cooperative Multi-Agent Reinforcement Learning”⁠, Wei Fu, Chao Yu, Zelai Xu, Jiaqi Yang, Yi Wu2022-06-15 (similar; bibliography)

“MAT: Multi-Agent Reinforcement Learning Is a Sequence Modeling Problem”, Wen Et Al 2022

“MAT: Multi-Agent Reinforcement Learning is a Sequence Modeling Problem”⁠, Muning Wen, Jakub Grudzien Kuba, Runji Lin, Weinan Zhang, Ying Wen, Jun Wang, Yaodong Yang2022-05-30 (Decision Transformer; similar; bibliography)

“Emergent Bartering Behaviour in Multi-Agent Reinforcement Learning”, Johanson Et Al 2022

“Emergent Bartering Behaviour in Multi-Agent Reinforcement Learning”⁠, Michael Bradley Johanson, Edward Hughes, Finbarr Timbers, Joel Z. Leibo2022-05-13 (economics; similar)

“NeuPL: Neural Population Learning”, Liu Et Al 2022

“NeuPL: Neural Population Learning”⁠, Siqi Liu, Luke Marris, Daniel Hennes, Josh Merel, Nicolas Heess, Thore Graepel2022-02-15 (NN sparsity⁠, RL exploration⁠, meta-learning⁠, Decision Transformer; similar; bibliography)

“Uncalibrated Models Can Improve Human-AI Collaboration”, Vodrahalli Et Al 2022

“Uncalibrated Models Can Improve Human-AI Collaboration”⁠, Kailas Vodrahalli, Tobias Gerstenberg, James Zou2022-02-12 (AI safety⁠, forecasting; similar)

“Hidden Agenda: a Social Deduction Game With Diverse Learned Equilibria”, Kopparapu Et Al 2022

“Hidden Agenda: a Social Deduction Game with Diverse Learned Equilibria”⁠, Kavya Kopparapu, Edgar A. Duéñez-Guzmán, Jayd Matyas, Alexander Sasha Vezhnevets, John P. Agapiou, Kevin R. McKee2022-01-05 (backlinks; similar)

“Finding General Equilibria in Many-Agent Economic Simulations Using Deep Reinforcement Learning”, Curry Et Al 2022

“Finding General Equilibria in Many-Agent Economic Simulations Using Deep Reinforcement Learning”⁠, Michael Curry, Alexander Trott, Soham Phade, Yu Bai, Stephan Zheng2022-01-03 (economics⁠, meta-learning; similar)

“Maximum Entropy Population Based Training for Zero-Shot Human-AI Coordination”, Zhao Et Al 2021

“Maximum Entropy Population Based Training for Zero-Shot Human-AI Coordination”⁠, Rui Zhao, Jinming Song, Hu Haifeng, Yang Gao, Yi Wu, Zhongqian Sun, Yang Wei2021-12-22 (RL exploration; similar; bibliography)

“Modeling Strong and Human-Like Gameplay With KL-Regularized Search”, Jacob Et Al 2021

“Modeling Strong and Human-Like Gameplay with KL-Regularized Search”⁠, Athul Paul Jacob, David J. Wu, Gabriele Farina, Adam Lerer, Anton Bakhtin, Jacob Andreas, Noam Brown2021-12-14 (AI chess⁠, Diplomacy AI⁠, preference learning; similar)

“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, AlphaGo; similar; bibliography)

“Offline Pre-trained Multi-Agent Decision Transformer: One Big Sequence Model Tackles All SMAC Tasks”, Meng Et Al 2021

“Offline Pre-trained Multi-Agent Decision Transformer: One Big Sequence Model Tackles All SMAC Tasks”⁠, Linghui Meng, Muning Wen, Yaodong Yang, Chenyang Le, Xiyun Li, Weinan Zhang, Ying Wen, Haifeng Zhang2021-12-06 (Decision Transformer; backlinks; similar)

“Collective Intelligence for Deep Learning: A Survey of Recent Developments”, Ha & Tang2021

“Collective Intelligence for Deep Learning: A Survey of Recent Developments”⁠, David Ha, Yujin Tang2021-11-29 (cellular automata; similar)

“Learning to Ground Multi-Agent Communication With Autoencoders”, Lin Et Al 2021

“Learning to Ground Multi-Agent Communication with Autoencoders”⁠, Toru Lin, Minyoung Huh, Chris Stauffer, Ser-Nam Lim, Phillip Isola2021-10-28 (backlinks; similar)

“Meta-learning, Social Cognition and Consciousness in Brains and Machines”, Langdon Et Al 2021

“Meta-learning, social cognition and consciousness in brains and machines”⁠, Angela Langdon, Matthew Botvinick, Hiroyuki Nakahara, Keiji Tanaka, Masayuki Matsumoto, Ryota Kanai2021-10-18 (meta-learning; backlinks; similar)

“Collaborating With Humans without Human Data”, Strouse Et Al 2021

“Collaborating with Humans without Human Data”⁠, DJ Strouse, Kevin R. McKee, Matt Botvinick, Edward Hughes, Richard Everett2021-10-15 (RL scaling; similar)

“The Neural MMO Platform for Massively Multiagent Research”, Suarez Et Al 2021

“The Neural MMO Platform for Massively Multiagent Research”⁠, Joseph Suarez, Yilun Du, Clare Zhu, Igor Mordatch, Phillip Isola2021-10-14 (backlinks; similar)

“Embodied Intelligence via Learning and Evolution”, Gupta Et Al 2021

“Embodied intelligence via learning and evolution”⁠, Agrim Gupta, Silvio Savarese, Surya Ganguli, Li Fei-Fei2021-10-06 (meta-learning; backlinks; similar)

“No-Press Diplomacy from Scratch”, Bakhtin Et Al 2021

“No-Press Diplomacy from Scratch”⁠, Anton Bakhtin, David Wu, Adam Lerer, Noam Brown2021-10-06 (Diplomacy AI; similar)

“Replay-Guided Adversarial Environment Design”, Jiang Et Al 2021

“Replay-Guided Adversarial Environment Design”⁠, Minqi Jiang, Michael Dennis, Jack Parker-Holder, Jakob Foerster, Edward Grefenstette, Tim Rocktäschel2021-10-06 (meta-learning; similar)

“Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning”, Kuba Et Al 2021

“Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning”⁠, Jakub Grudzien Kuba, Ruiqing Chen, Muning Wen, Ying Wen, Fanglei Sun, Jun Wang, Yaodong Yang2021-09-23 (backlinks; similar)

“WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU”, Lan Et Al 2021

“WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU”⁠, Tian Lan, Sunil Srinivasa, Stephan Zheng2021-08-31 (AI scaling/hardware⁠, RL scaling; similar)

“The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement Learning”, Zheng Et Al 2021

“The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement Learning”⁠, Stephan Zheng, Alexander Trott, Sunil Srinivasa, David C. Parkes, Richard Socher2021-08-05 (meta-learning; similar)

“Open-Ended Learning Leads to Generally Capable Agents”, Team Et Al 2021

“Open-Ended Learning Leads to Generally Capable Agents”⁠, Open Ended Learning Team, Adam Stooke, Anuj Mahajan, Catarina Barros, Charlie Deck, Jakob Bauer, Jakub Sygnowski2021-07-27 (RL exploration⁠, meta-learning⁠, RL scaling; similar)

“Megaverse: Simulating Embodied Agents at One Million Experiences per Second”, Petrenko Et Al 2021

“Megaverse: Simulating Embodied Agents at One Million Experiences per Second”⁠, Aleksei Petrenko, Erik Wijmans, Brennan Shacklett, Vladlen Koltun2021-07-17 (CS/hardware⁠, model-free RL⁠, RL scaling; similar)

“Scalable Evaluation of Multi-Agent Reinforcement Learning With Melting Pot”, Leibo Et Al 2021

“Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot”⁠, Joel Z. Leibo, Edgar Duéñez-Guzmán, Alexander Sasha Vezhnevets, John P. Agapiou, Peter Sunehag, Raphael Koster2021-07-14 (similar)

“From Motor Control to Team Play in Simulated Humanoid Football”, Liu Et Al 2021

“From Motor Control to Team Play in Simulated Humanoid Football”⁠, Siqi Liu, Guy Lever, Zhe Wang, Josh Merel, S. M. Ali Eslami, Daniel Hennes, Wojciech M. Czarnecki, Yuval Tassa2021-05-25 (RL exploration⁠, RL scaling; similar; bibliography)

“Cooperative AI Foundation (CAIF)”, CAIF 2021

“Cooperative AI Foundation (CAIF)”⁠, CAIF2021-05-04 (AI scaling/economics; similar)

“Baller2vec++: A Look-Ahead Multi-Entity Transformer For Modeling Coordinated Agents”, Alcorn & Nguyen2021

“baller2vec++: A Look-Ahead Multi-Entity Transformer For Modeling Coordinated Agents”⁠, Michael A. Alcorn, Anh Nguyen2021-04-24 (Decision Transformer; similar; bibliography)

“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 (AlphaGo⁠, robotics; similar)

“Multitasking Inhibits Semantic Drift”, Jacob Et Al 2021

“Multitasking Inhibits Semantic Drift”⁠, Athul Paul Jacob, Mike Lewis, Jacob Andreas2021-04-15 (AI safety; backlinks; similar)

“Asymmetric Self-play for Automatic Goal Discovery in Robotic Manipulation”, OpenAI Et Al 2021

“Asymmetric self-play for automatic goal discovery in robotic manipulation”⁠, OpenAI OpenAI, Matthias Plappert, Raul Sampedro, Tao Xu, Ilge Akkaya, Vineet Kosaraju, Peter Welinder2021-03-05 (RL exploration⁠, meta-learning⁠, robotics; similar)

“Reinforcement Learning for Datacenter Congestion Control”, Tessler Et Al 2021

“Reinforcement Learning for Datacenter Congestion Control”⁠, Chen Tessler, Yuval Shpigelman, Gal Dalal, Amit Mandelbaum, Doron Haritan Kazakov, Benjamin Fuhrer, Gal Chechik2021-02-18 (model-free RL; similar)

“Baller2vec: A Multi-Entity Transformer For Multi-Agent Spatiotemporal Modeling”, Alcorn & Nguyen2021

“baller2vec: A Multi-Entity Transformer For Multi-Agent Spatiotemporal Modeling”⁠, Michael A. Alcorn, Anh Nguyen2021-02-05 (Transformer NN; backlinks; similar)

“UPDeT: Universal Multi-agent Reinforcement Learning via Policy Decoupling With Transformers”, Hu Et Al 2021

“UPDeT: Universal Multi-agent Reinforcement Learning via Policy Decoupling with Transformers”⁠, Siyi Hu, Fengda Zhu, Xiaojun Chang, Xiaodan Liang2021-01-20 (Transformer NN; similar)

“Imitating Interactive Intelligence”, Abramson Et Al 2020

“Imitating Interactive Intelligence”⁠, Interactive Agents Group (Josh Abramson, Arun Ahuja, Arthur Brussee, Federico Carnevale, Mary Cassin2020-12-10 (RL exploration⁠, robotics⁠, RL scaling; similar; bibliography)

“TLeague: A Framework for Competitive Self-Play Based Distributed Multi-Agent Reinforcement Learning”, Sun Et Al 2020

“TLeague: A Framework for Competitive Self-Play based Distributed Multi-Agent Reinforcement Learning”⁠, Peng Sun, Jiechao Xiong, Lei Han, Xinghai Sun, Shuxing Li, Jiawei Xu, Meng Fang, Zhengyou Zhang2020-11-25 (AlphaStar; similar; bibliography)

“Towards Playing Full MOBA Games With Deep Reinforcement Learning”, Ye Et Al 2020

“Towards Playing Full MOBA Games with Deep Reinforcement Learning”⁠, Deheng Ye, Guibin Chen, Wen Zhang, Sheng Chen, Bo Yuan, Bo Liu, Jia Chen, Zhao Liu, Fuhao Qiu, Hongsheng Yu2020-11-25 (RNN⁠, knowledge distillation⁠, OA5; similar; bibliography)

“Emergent Road Rules In Multi-Agent Driving Environments”, Pal Et Al 2020

“Emergent Road Rules In Multi-Agent Driving Environments”⁠, Avik Pal, Jonah Philion, Yuan-Hong Liao, Sanja Fidler2020-11-21 (AI safety; similar)

“Reinforcement Learning for Optimization of COVID-19 Mitigation Policies”, Kompella Et Al 2020

“Reinforcement Learning for Optimization of COVID-19 Mitigation policies”⁠, Varun Kompella, Roberto Capobianco, Stacy Jong, Jonathan Browne, Spencer Fox, Lauren Meyers, Peter Wurman2020-10-20 (backlinks; similar)

“Human-Level Performance in No-Press Diplomacy via Equilibrium Search”, Gray Et Al 2020

“Human-Level Performance in No-Press Diplomacy via Equilibrium Search”⁠, Jonathan Gray, Adam Lerer, Anton Bakhtin, Noam Brown2020-10-06 (Diplomacy AI; similar)

“Emergent Social Learning via Multi-agent Reinforcement Learning”, Ndousse Et Al 2020

“Emergent Social Learning via Multi-agent Reinforcement Learning”⁠, Kamal Ndousse, Douglas Eck, Sergey Levine, Natasha Jaques2020-10-01 (model-free RL; similar)

“Grounded Language Learning Fast and Slow”, Hill Et Al 2020

“Grounded Language Learning Fast and Slow”⁠, Felix Hill, Olivier Tieleman, Tamara von Glehn, Nathaniel Wong, Hamza Merzic, Stephen Clark2020-09-03 (meta-learning; 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, AlphaGo; similar)

“Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions [blog]”, Chang & Kaushik2020

“Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions [blog]”⁠, Michael Chang, Sidhant Kaushik2020-07-11 (backlinks; similar; bibliography)

“One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control”, Huang Et Al 2020

“One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control”⁠, Wenlong Huang, Igor Mordatch, Deepak Pathak2020-07-09 (backlinks; similar)

“Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions”, Chang Et Al 2020

“Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions”⁠, Michael Chang, Sidhant Kaushik, S. Matthew Weinberg, Thomas L. Griffiths, Sergey Levine2020-07-05 (meta-learning; backlinks; similar)

“Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks”, Papoudakis Et Al 2020

“Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks”⁠, Georgios Papoudakis, Filippos Christianos, Lukas Schäfer, Stefano V. Albrecht2020-06-14 (backlinks; similar)

“Learning to Play No-Press Diplomacy With Best Response Policy Iteration”, Anthony Et Al 2020

“Learning to Play No-Press Diplomacy with Best Response Policy Iteration”⁠, Thomas Anthony, Tom Eccles, Andrea Tacchetti, János Kramár, Ian Gemp, Thomas C. Hudson, Nicolas Porcel2020-06-08 (Diplomacy AI; 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, AlphaGo; 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, AlphaGo; similar)

“Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and Their Solutions”, Wang Et Al 2020

“Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions”⁠, Rui Wang, Joel Lehman, Aditya Rawal, Jiale Zhi, Yulun Li, Jeff Clune, Kenneth O. Stanley2020-03-19 (RL exploration⁠, meta-learning; similar)

“Social Diversity and Social Preferences in Mixed-motive Reinforcement Learning”, McKee Et Al 2020

“Social diversity and social preferences in mixed-motive reinforcement learning”⁠, Kevin R. McKee, Ian Gemp, Brian McWilliams, Edgar A. Duéñez-Guzmán, Edward Hughes, Joel Z. Leibo2020-02-06 (similar)

“Effective Diversity in Population Based Reinforcement Learning”, Parker-Holder Et Al 2020

“Effective Diversity in Population Based Reinforcement Learning”⁠, Jack Parker-Holder, Aldo Pacchiano, Krzysztof Choromanski, Stephen Roberts2020-02-03 (RL exploration⁠, meta-learning; similar)

“Towards Learning Multi-agent Negotiations via Self-Play”, Tang2020

“Towards Learning Multi-agent Negotiations via Self-Play”⁠, Yichuan Charlie Tang2020-01-28 (similar)

“Smooth Markets: A Basic Mechanism for Organizing Gradient-based Learners”, Balduzzi Et Al 2020

“Smooth markets: A basic mechanism for organizing gradient-based learners”⁠, David Balduzzi, Wojciech M. Czarnecki, Thomas W. Anthony, Ian M. Gemp, Edward Hughes, Joel Z. Leibo, Georgios Piliouras2020-01-14 (GAN⁠, meta-learning; similar)

“Learning by Cheating”, Chen Et Al 2019

“Learning by Cheating”⁠, Dian Chen, Brady Zhou, Vladlen Koltun, Philipp Krähenbühl2019-12-27 (backlinks; similar)

“Increasing Generality in Machine Learning through Procedural Content Generation”, Risi & Togelius2019

“Increasing Generality in Machine Learning through Procedural Content Generation”⁠, Sebastian Risi, Julian Togelius2019-11-29 (meta-learning⁠, robotics; similar)

“Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms”, Zhang Et Al 2019

“Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms”⁠, Kaiqing Zhang, Zhuoran Yang, Tamer Başar2019-11-24 (poker AI; similar)

“Grandmaster Level in StarCraft II Using Multi-agent Reinforcement Learning”, Vinyals Et Al 2019

“Grandmaster level in StarCraft II using multi-agent reinforcement learning”⁠, Oriol Vinyals, Igor Babuschkin, Wojciech M. Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung2019-10-30 (AI scaling⁠, CS/algorithm⁠, AlphaStar; similar; bibliography)

“Stabilizing Generative Adversarial Networks: A Survey”, Wiatrak Et Al 2019

“Stabilizing Generative Adversarial Networks: A Survey”⁠, Maciej Wiatrak, Stefano V. Albrecht, Andrew Nystrom2019-09-30 (StyleGAN; backlinks; similar)

“Emergent Tool Use from Multi-Agent Interaction § Surprising Behavior”, Baker Et Al 2019

“Emergent Tool Use from Multi-Agent Interaction § Surprising behavior”⁠, Bowen Baker, Ingmar Kanitscheider, Todor Markov, Yi Wu, Glenn Powell, Bob McGrew, Igor Mordatch2019-09-17 (RL exploration⁠, RL scaling; similar; bibliography)

“Emergent Tool Use From Multi-Agent Autocurricula”, Baker Et Al 2019

“Emergent Tool Use From Multi-Agent Autocurricula”⁠, Bowen Baker, Ingmar Kanitscheider, Todor Markov, Yi Wu, Glenn Powell, Bob McGrew, Igor Mordatch2019-09-17 (neural net⁠, RL exploration⁠, meta-learning; similar)

“No Press Diplomacy: Modeling Multi-Agent Gameplay”, Paquette Et Al 2019

“No Press Diplomacy: Modeling Multi-Agent Gameplay”⁠, Philip Paquette, Yuchen Lu, Steven Bocco, Max O. Smith, Satya Ortiz-Gagne, Jonathan K. Kummerfeld, Satinder Singh2019-09-04 (ML dataset⁠, Diplomacy AI; similar)

“A Review of Cooperative Multi-Agent Deep Reinforcement Learning”, OroojlooyJadid & Hajinezhad2019

“A Review of Cooperative Multi-Agent Deep Reinforcement Learning”⁠, Afshin OroojlooyJadid, Davood Hajinezhad2019-08-11 (similar)

“Pluribus: Superhuman AI for Multiplayer Poker”, Brown & Sandholm2019

“Pluribus: Superhuman AI for multiplayer poker”⁠, Noam Brown, Tuomas Sandholm2019-07-11 (poker AI; similar)

“Evolving the Hearthstone Meta”, Silva Et Al 2019

“Evolving the Hearthstone Meta”⁠, Fernando de Mesentier Silva, Rodrigo Canaan, Scott Lee, Matthew C. Fontaine, Julian Togelius, Amy K. Hoover2019-07-02 (meta-learning; similar)

“Evolutionary Implementation of Bayesian Computations”, Czégel Et Al 2019

“Evolutionary implementation of Bayesian computations”⁠, Dániel Czégel, Hamza Giaffar, István Zachar, Eörs Szathmáry2019-06-28 (genetics/selection/natural⁠, Bayes; backlinks; similar)

“Finding Friend and Foe in Multi-Agent Games”, Serrino Et Al 2019

“Finding Friend and Foe in Multi-Agent Games”⁠, Jack Serrino, Max Kleiman-Weiner, David C. Parkes, Joshua B. Tenenbaum2019-06-05 (RL/imperfect-information; similar)

“Hierarchical Decision Making by Generating and Following Natural Language Instructions”, Hu Et Al 2019

“Hierarchical Decision Making by Generating and Following Natural Language Instructions”⁠, Hengyuan Hu, Denis Yarats, Qucheng Gong, Yuandong Tian, Mike Lewis2019-06-03 (similar)

“ICML 2019 Notes”, Abel2019

“ICML 2019 Notes”⁠, David Abel2019-06 (knowledge distillation⁠, AI scaling⁠, RL exploration⁠, meta-learning; similar; bibliography)

“Human-level Performance in 3D Multiplayer Games With Population-based Reinforcement Learning”, Jaderberg Et Al 2019

“Human-level performance in 3D multiplayer games with population-based reinforcement learning”⁠, Max Jaderberg, Wojciech M. Czarnecki, Iain Dunning, Luke Marris, Guy Lever, Antonio Garcia Castañeda2019-05-31 (CNN⁠, RL exploration⁠, AlphaStar⁠, RL scaling; similar; bibliography)

“AI-GAs: AI-generating Algorithms, an Alternate Paradigm for Producing General Artificial Intelligence”, Clune2019

“AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence”⁠, Jeff Clune2019-05-27 (RL exploration⁠, meta-learning⁠, robotics⁠, AI safety⁠, RL scaling; similar)

“Adversarial Policies: Attacking Deep Reinforcement Learning”, Gleave Et Al 2019

“Adversarial Policies: Attacking Deep Reinforcement Learning”⁠, Adam Gleave, Michael Dennis, Cody Wild, Neel Kant, Sergey Levine, Stuart Russell2019-05-25 (neural net/adversarial⁠, robotics; 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, AlphaGo; similar)

“Autocurricula and the Emergence of Innovation from Social Interaction: A Manifesto for Multi-Agent Intelligence Research”, Leibo Et Al 2019

“Autocurricula and the Emergence of Innovation from Social Interaction: A Manifesto for Multi-Agent Intelligence Research”⁠, Joel Z. Leibo, Edward Hughes, Marc Lanctot, Thore Graepel2019-03-02 (similar)

“Distilling Policy Distillation”, Czarnecki Et Al 2019

“Distilling Policy Distillation”⁠, Wojciech Marian Czarnecki, Razvan Pascanu, Simon Osindero, Siddhant M. Jayakumar, Grzegorz Swirszcz, Max Jaderberg2019-02-06 (knowledge distillation; similar; bibliography)

“Open-ended Learning in Symmetric Zero-sum Games”, Balduzzi Et Al 2019

“Open-ended Learning in Symmetric Zero-sum Games”⁠, David Balduzzi, Marta Garnelo, Yoram Bachrach, Wojciech M. Czarnecki, Julien Perolat, Max Jaderberg, Thore Graepel2019-01-23 (similar)

“Hierarchical Reinforcement Learning for Multi-agent MOBA Game”, Zhang Et Al 2019

“Hierarchical Reinforcement Learning for Multi-agent MOBA Game”⁠, Zhijian Zhang, Haozheng Li, Luo Zhang, Tianyin Zheng, Ting Zhang, Xiong Hao, Xiaoxin Chen, Min Chen, Fangxu Xiao2019-01-23 (similar)

“Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions”, Wang Et Al 2019

“Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions”⁠, Rui Wang, Joel Lehman, Jeff Clune, Kenneth O. Stanley2019-01-07 (RL exploration⁠, meta-learning; similar)

“Hierarchical Macro Strategy Model for MOBA Game AI”, Wu Et Al 2018

“Hierarchical Macro Strategy Model for MOBA Game AI”⁠, Bin Wu, Qiang Fu, Jing Liang, Peng Qu, Xiaoqian Li, Liang Wang, Wei Liu, Wei Yang, Yongsheng Liu2018-12-19 (similar)

“Continual Match Based Training in Pommerman: Technical Report”, Peng Et Al 2018

“Continual Match Based Training in Pommerman: Technical Report”⁠, Peng Peng, Liang Pang, Yufeng Yuan, Chao Gao2018-12-18 (similar)

“Malthusian Reinforcement Learning”, Leibo Et Al 2018

“Malthusian Reinforcement Learning”⁠, Joel Z. Leibo, Julien Perolat, Edward Hughes, Steven Wheelwright, Adam H. Marblestone, Edgar Duéñez-Guzmán2018-12-17 (meta-learning; similar)

“Evolution As Backstop for Reinforcement Learning”, Branwen2018

“Evolution as Backstop for Reinforcement Learning”⁠, Gwern Branwen2018-12-06 (neural net⁠, biology⁠, economics/automation⁠, insight porn⁠, philosophy/epistemology⁠, mental energy⁠, psych/willpower⁠, AI safety⁠, Bayes⁠, decision theory⁠, tech; backlinks; similar; bibliography)

“Stable Opponent Shaping in Differentiable Games”, Letcher Et Al 2018

“Stable Opponent Shaping in Differentiable Games”⁠, Alistair Letcher, Jakob Foerster, David Balduzzi, Tim Rocktäschel, Shimon Whiteson2018-11-20 (similar)

“Deep Counterfactual Regret Minimization”, Brown Et Al 2018

“Deep Counterfactual Regret Minimization”⁠, Noam Brown, Adam Lerer, Sam Gross, Tuomas Sandholm2018-11-01 (poker AI; similar)

“TarMAC: Targeted Multi-Agent Communication”, Das Et Al 2018

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Wikipedia

1. Diplomacy (game)

2. Fundamental theorem of poker

3. Hanabi (card game)

4. Poker

5. Scotland Yard