- See Also
-
Links
- “Melting Pot 2.0”, Et Al 2022
- “CICERO: Human-level Play in the Game Of Diplomacy By Combining Language Models With Strategic Reasoning”, Et Al 2022
- “Over-communicate No More: Situated RL Agents Learn Concise Communication Protocols”, Et Al 2022
- “Human-AI Coordination via Human-Regularized Search and Learning”, Et Al 2022
- “Game Theoretic Rating in N-player General-sum Games With Equilibria”, Et Al 2022
- “Modeling Bounded Rationality in Multi-Agent Simulations Using Rationally Inattentive Reinforcement Learning”, 2022
- “Neural Payoff Machines: Predicting Fair and Stable Payoff Allocations Among Team Members”, Et Al 2022
- “Social Simulacra: Creating Populated Prototypes for Social Computing Systems”, Et Al 2022
- “DeepNash: Mastering the Game of Stratego With Model-Free Multiagent Reinforcement Learning”, Et Al 2022
- “Fleet-DAgger: Interactive Robot Fleet Learning With Scalable Human Supervision”, Et Al 2022
- “Revisiting Some Common Practices in Cooperative Multi-Agent Reinforcement Learning”, Et Al 2022
- “MAT: Multi-Agent Reinforcement Learning Is a Sequence Modeling Problem”, Et Al 2022
- “Emergent Bartering Behaviour in Multi-Agent Reinforcement Learning”, Et Al 2022
- “NeuPL: Neural Population Learning”, Et Al 2022
- “Uncalibrated Models Can Improve Human-AI Collaboration”, Et Al 2022
- “Hidden Agenda: a Social Deduction Game With Diverse Learned Equilibria”, Et Al 2022
- “Finding General Equilibria in Many-Agent Economic Simulations Using Deep Reinforcement Learning”, Et Al 2022
- “Maximum Entropy Population Based Training for Zero-Shot Human-AI Coordination”, Et Al 2021
- “Modeling Strong and Human-Like Gameplay With KL-Regularized Search”, Et Al 2021
- “Player of Games”, Et Al 2021
- “Offline Pre-trained Multi-Agent Decision Transformer: One Big Sequence Model Tackles All SMAC Tasks”, Et Al 2021
- “Collective Intelligence for Deep Learning: A Survey of Recent Developments”, 2021
- “Learning to Ground Multi-Agent Communication With Autoencoders”, Et Al 2021
- “Meta-learning, Social Cognition and Consciousness in Brains and Machines”, Et Al 2021
- “Collaborating With Humans without Human Data”, Et Al 2021
- “The Neural MMO Platform for Massively Multiagent Research”, Et Al 2021
- “Embodied Intelligence via Learning and Evolution”, Et Al 2021
- “No-Press Diplomacy from Scratch”, Et Al 2021
- “Replay-Guided Adversarial Environment Design”, Et Al 2021
- “Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning”, Et Al 2021
- “WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU”, Et Al 2021
- “The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement Learning”, Et Al 2021
- “Open-Ended Learning Leads to Generally Capable Agents”, Et Al 2021
- “Megaverse: Simulating Embodied Agents at One Million Experiences per Second”, Et Al 2021
- “Scalable Evaluation of Multi-Agent Reinforcement Learning With Melting Pot”, Et Al 2021
- “From Motor Control to Team Play in Simulated Humanoid Football”, Et Al 2021
- “Cooperative AI Foundation (CAIF)”, CAIF 2021
- “Baller2vec++: A Look-Ahead Multi-Entity Transformer For Modeling Coordinated Agents”, 2021
- “Neural Tree Expansion for Multi-Robot Planning in Non-Cooperative Environments”, Et Al 2021
- “Multitasking Inhibits Semantic Drift”, Et Al 2021
- “Asymmetric Self-play for Automatic Goal Discovery in Robotic Manipulation”, OpenAI Et Al 2021
- “Reinforcement Learning for Datacenter Congestion Control”, Et Al 2021
- “Baller2vec: A Multi-Entity Transformer For Multi-Agent Spatiotemporal Modeling”, 2021
- “UPDeT: Universal Multi-agent Reinforcement Learning via Policy Decoupling With Transformers”, Et Al 2021
- “Imitating Interactive Intelligence”, Et Al 2020
- “TLeague: A Framework for Competitive Self-Play Based Distributed Multi-Agent Reinforcement Learning”, Et Al 2020
- “Towards Playing Full MOBA Games With Deep Reinforcement Learning”, Et Al 2020
- “Emergent Road Rules In Multi-Agent Driving Environments”, Et Al 2020
- “Reinforcement Learning for Optimization of COVID-19 Mitigation Policies”, Et Al 2020
- “Human-Level Performance in No-Press Diplomacy via Equilibrium Search”, Et Al 2020
- “Emergent Social Learning via Multi-agent Reinforcement Learning”, Et Al 2020
- “Grounded Language Learning Fast and Slow”, Et Al 2020
- “ReBeL: Combining Deep Reinforcement Learning and Search for Imperfect-Information Games”, Et Al 2020
- “Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions [blog]”, 2020
- “One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control”, Et Al 2020
- “Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions”, Et Al 2020
- “Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks”, Et Al 2020
- “Learning to Play No-Press Diplomacy With Best Response Policy Iteration”, Et Al 2020
- “Approximate Exploitability: Learning a Best Response in Large Games”, Et Al 2020
- “Real World Games Look Like Spinning Tops”, Et Al 2020
- “Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and Their Solutions”, Et Al 2020
- “Social Diversity and Social Preferences in Mixed-motive Reinforcement Learning”, Et Al 2020
- “Effective Diversity in Population Based Reinforcement Learning”, Parker-Et Al 2020
- “Towards Learning Multi-agent Negotiations via Self-Play”, 2020
- “Smooth Markets: A Basic Mechanism for Organizing Gradient-based Learners”, Et Al 2020
- “Learning by Cheating”, Et Al 2019
- “Increasing Generality in Machine Learning through Procedural Content Generation”, 2019
- “Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms”, Et Al 2019
- “Grandmaster Level in StarCraft II Using Multi-agent Reinforcement Learning”, Et Al 2019
- “Stabilizing Generative Adversarial Networks: A Survey”, Et Al 2019
- “Emergent Tool Use from Multi-Agent Interaction § Surprising Behavior”, Et Al 2019
- “Emergent Tool Use From Multi-Agent Autocurricula”, Et Al 2019
- “No Press Diplomacy: Modeling Multi-Agent Gameplay”, Et Al 2019
- “A Review of Cooperative Multi-Agent Deep Reinforcement Learning”, Oroojlooy2019
- “Pluribus: Superhuman AI for Multiplayer Poker”, 2019
- “Evolving the Hearthstone Meta”, Et Al 2019
- “Evolutionary Implementation of Bayesian Computations”, Et Al 2019
- “Finding Friend and Foe in Multi-Agent Games”, Et Al 2019
- “Hierarchical Decision Making by Generating and Following Natural Language Instructions”, Et Al 2019
- “ICML 2019 Notes”, 2019
- “Human-level Performance in 3D Multiplayer Games With Population-based Reinforcement Learning”, Et Al 2019
- “AI-GAs: AI-generating Algorithms, an Alternate Paradigm for Producing General Artificial Intelligence”, 2019
- “Adversarial Policies: Attacking Deep Reinforcement Learning”, Et Al 2019
- “Α-Rank: Multi-Agent Evaluation by Evolution”, Et Al 2019
- “Autocurricula and the Emergence of Innovation from Social Interaction: A Manifesto for Multi-Agent Intelligence Research”, Et Al 2019
- “Distilling Policy Distillation”, Et Al 2019
- “Open-ended Learning in Symmetric Zero-sum Games”, Et Al 2019
- “Hierarchical Reinforcement Learning for Multi-agent MOBA Game”, Et Al 2019
- “Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions”, Et Al 2019
- “Hierarchical Macro Strategy Model for MOBA Game AI”, Et Al 2018
- “Continual Match Based Training in Pommerman: Technical Report”, Et Al 2018
- “Malthusian Reinforcement Learning”, Et Al 2018
- “Evolution As Backstop for Reinforcement Learning”, 2018
- “Stable Opponent Shaping in Differentiable Games”, Et Al 2018
- “Deep Counterfactual Regret Minimization”, Et Al 2018
- “TarMAC: Targeted Multi-Agent Communication”, Et Al 2018
- “Graph Convolutional Reinforcement Learning”, Et Al 2018
- “Social Influence As Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning”, Et Al 2018
- “Deep Reinforcement Learning”, 2018
- “A Survey and Critique of Multiagent Deep Reinforcement Learning”, Hernandez-Et Al 2018
- “Learning to Coordinate Multiple Reinforcement Learning Agents for Diverse Query Reformulation”, Et Al 2018
- “Pommerman: A Multi-Agent Playground”, Et Al 2018
- “Fully Distributed Multi-Robot Collision Avoidance via Deep Reinforcement Learning for Safe and Efficient Navigation in Complex Scenarios”, Et Al 2018
- “Human-level Performance in First-person Multiplayer Games With Population-based Deep Reinforcement Learning”, Et Al 2018
- “Construction of Arbitrarily Strong Amplifiers of Natural Selection Using Evolutionary Graph Theory”, Et Al 2018
- “Adaptive Mechanism Design: Learning to Promote Cooperation”, Et Al 2018
- “Mix&Match—Agent Curricula for Reinforcement Learning”, Et Al 2018
- “Kickstarting Deep Reinforcement Learning”, Et Al 2018
- “Machine Theory of Mind”, Et Al 2018
- “Sim-to-Real Optimization of Complex Real World Mobile Network With Imperfect Information via Deep Reinforcement Learning from Self-play”, Et Al 2018
- “Trust-Aware Decision Making for Human-Robot Collaboration: Model Learning and Planning”, Et Al 2018
- “Emergent Complexity via Multi-Agent Competition”, Et Al 2017
- “Learning With Opponent-Learning Awareness”, Et Al 2017
- “LADDER: A Human-Level Bidding Agent for Large-Scale Real-Time Online Auctions”, Et Al 2017
- “CAN: Creative Adversarial Networks, Generating”Art” by Learning About Styles and Deviating from Style Norms”, Et Al 2017
- “Supervision via Competition: Robot Adversaries for Learning Tasks”, Et Al 2016
- “Policy Distillation”, Et Al 2015
- “Reflective Oracles: A Foundation for Classical Game Theory”, Et Al 2015
- “One Writer Enters International Competition to Play the World-conquering Game That Redefines What It Means to Be a Geek (and a Person)”
- Wikipedia
- Miscellaneous
- Link Bibliography
See Also
Links
“Melting Pot 2.0”, Et Al 2022
“Melting Pot 2.0”, 2022-11-24 ( ; similar)
“CICERO: Human-level Play in the Game Of Diplomacy By Combining Language Models With Strategic Reasoning”, Et Al 2022
“CICERO: Human-level play in the game of Diplomacy by combining language models with strategic reasoning”, 2022-11-22 ( ; similar; bibliography)
“Over-communicate No More: Situated RL Agents Learn Concise Communication Protocols”, Et Al 2022
“Over-communicate no more: Situated RL agents learn concise communication protocols”, 2022-11-02 ( ; similar)
“Human-AI Coordination via Human-Regularized Search and Learning”, Et Al 2022
“Human-AI Coordination via Human-Regularized Search and Learning”, 2022-10-11 ( ; similar)
“Game Theoretic Rating in N-player General-sum Games With Equilibria”, Et Al 2022
“Game Theoretic Rating in N-player general-sum games with Equilibria”, 2022-10-05 (similar)
“Modeling Bounded Rationality in Multi-Agent Simulations Using Rationally Inattentive Reinforcement Learning”, 2022
“Modeling Bounded Rationality in Multi-Agent Simulations Using Rationally Inattentive Reinforcement Learning”, 2022-09-19 ( ; similar; bibliography)
“Neural Payoff Machines: Predicting Fair and Stable Payoff Allocations Among Team Members”, Et Al 2022
“Neural Payoff Machines: Predicting Fair and Stable Payoff Allocations Among Team Members”, 2022-08-18 ( ; similar)
“Social Simulacra: Creating Populated Prototypes for Social Computing Systems”, Et Al 2022
“Social Simulacra: Creating Populated Prototypes for Social Computing Systems”, 2022-08-08 ( ; backlinks; similar; bibliography)
“DeepNash: Mastering the Game of Stratego With Model-Free Multiagent Reinforcement Learning”, Et Al 2022
“DeepNash: Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning”, 2022-06-30 ( ; similar; bibliography)
“Fleet-DAgger: Interactive Robot Fleet Learning With Scalable Human Supervision”, Et Al 2022
“Fleet-DAgger: Interactive Robot Fleet Learning with Scalable Human Supervision”, 2022-06-29 ( ; similar)
“Revisiting Some Common Practices in Cooperative Multi-Agent Reinforcement Learning”, Et Al 2022
“Revisiting Some Common Practices in Cooperative Multi-Agent Reinforcement Learning”, 2022-06-15 (similar; bibliography)
“MAT: Multi-Agent Reinforcement Learning Is a Sequence Modeling Problem”, Et Al 2022
“MAT: Multi-Agent Reinforcement Learning is a Sequence Modeling Problem”, 2022-05-30 ( ; similar; bibliography)
“Emergent Bartering Behaviour in Multi-Agent Reinforcement Learning”, Et Al 2022
“Emergent Bartering Behaviour in Multi-Agent Reinforcement Learning”, 2022-05-13 ( ; similar)
“NeuPL: Neural Population Learning”, Et Al 2022
“NeuPL: Neural Population Learning”, 2022-02-15 ( ; similar; bibliography)
“Uncalibrated Models Can Improve Human-AI Collaboration”, Et Al 2022
“Uncalibrated Models Can Improve Human-AI Collaboration”, 2022-02-12 ( ; similar)
“Hidden Agenda: a Social Deduction Game With Diverse Learned Equilibria”, Et Al 2022
“Hidden Agenda: a Social Deduction Game with Diverse Learned Equilibria”, 2022-01-05 (backlinks; similar)
“Finding General Equilibria in Many-Agent Economic Simulations Using Deep Reinforcement Learning”, Et Al 2022
“Finding General Equilibria in Many-Agent Economic Simulations Using Deep Reinforcement Learning”, 2022-01-03 ( ; similar)
“Maximum Entropy Population Based Training for Zero-Shot Human-AI Coordination”, Et Al 2021
“Maximum Entropy Population Based Training for Zero-Shot Human-AI Coordination”, 2021-12-22 ( ; similar; bibliography)
“Modeling Strong and Human-Like Gameplay With KL-Regularized Search”, Et Al 2021
“Modeling Strong and Human-Like Gameplay with KL-Regularized Search”, 2021-12-14 ( ; similar)
“Player of Games”, Et Al 2021
“Player of Games”, 2021-12-06 ( ; similar; bibliography)
“Offline Pre-trained Multi-Agent Decision Transformer: One Big Sequence Model Tackles All SMAC Tasks”, Et Al 2021
“Offline Pre-trained Multi-Agent Decision Transformer: One Big Sequence Model Tackles All SMAC Tasks”, 2021-12-06 ( ; backlinks; similar)
“Collective Intelligence for Deep Learning: A Survey of Recent Developments”, 2021
“Collective Intelligence for Deep Learning: A Survey of Recent Developments”, 2021-11-29 ( ; similar)
“Learning to Ground Multi-Agent Communication With Autoencoders”, Et Al 2021
“Learning to Ground Multi-Agent Communication with Autoencoders”, 2021-10-28 (backlinks; similar)
“Meta-learning, Social Cognition and Consciousness in Brains and Machines”, Et Al 2021
“Meta-learning, social cognition and consciousness in brains and machines”, 2021-10-18 ( ; backlinks; similar)
“Collaborating With Humans without Human Data”, Et Al 2021
“Collaborating with Humans without Human Data”, 2021-10-15 ( ; similar)
“The Neural MMO Platform for Massively Multiagent Research”, Et Al 2021
“The Neural MMO Platform for Massively Multiagent Research”, 2021-10-14 (backlinks; similar)
“Embodied Intelligence via Learning and Evolution”, Et Al 2021
“Embodied intelligence via learning and evolution”, 2021-10-06 ( ; backlinks; similar)
“No-Press Diplomacy from Scratch”, Et Al 2021
“No-Press Diplomacy from Scratch”, 2021-10-06 ( ; similar)
“Replay-Guided Adversarial Environment Design”, Et Al 2021
“Replay-Guided Adversarial Environment Design”, 2021-10-06 ( ; similar)
“Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning”, Et Al 2021
“Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning”, 2021-09-23 (backlinks; similar)
“WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU”, Et Al 2021
“WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU”, 2021-08-31 ( ; similar)
“The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement Learning”, Et Al 2021
“The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement Learning”, 2021-08-05 ( ; similar)
“Open-Ended Learning Leads to Generally Capable Agents”, Et Al 2021
“Open-Ended Learning Leads to Generally Capable Agents”, 2021-07-27 ( ; similar)
“Megaverse: Simulating Embodied Agents at One Million Experiences per Second”, Et Al 2021
“Megaverse: Simulating Embodied Agents at One Million Experiences per Second”, 2021-07-17 ( ; similar)
“Scalable Evaluation of Multi-Agent Reinforcement Learning With Melting Pot”, Et Al 2021
“Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot”, 2021-07-14 (similar)
“From Motor Control to Team Play in Simulated Humanoid Football”, Et Al 2021
“From Motor Control to Team Play in Simulated Humanoid Football”, 2021-05-25 ( ; similar; bibliography)
“Cooperative AI Foundation (CAIF)”, CAIF 2021
“Cooperative AI Foundation (CAIF)”, 2021-05-04 ( ; similar)
“Baller2vec++: A Look-Ahead Multi-Entity Transformer For Modeling Coordinated Agents”, 2021
“baller2vec++: A Look-Ahead Multi-Entity Transformer For Modeling Coordinated Agents”, 2021-04-24 ( ; similar; bibliography)
“Neural Tree Expansion for Multi-Robot Planning in Non-Cooperative Environments”, Et Al 2021
“Neural Tree Expansion for Multi-Robot Planning in Non-Cooperative Environments”, 2021-04-20 ( ; similar)
“Multitasking Inhibits Semantic Drift”, Et Al 2021
“Multitasking Inhibits Semantic Drift”, 2021-04-15 ( ; 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”, 2021-03-05 ( ; similar)
“Reinforcement Learning for Datacenter Congestion Control”, Et Al 2021
“Reinforcement Learning for Datacenter Congestion Control”, 2021-02-18 ( ; similar)
“Baller2vec: A Multi-Entity Transformer For Multi-Agent Spatiotemporal Modeling”, 2021
“baller2vec: A Multi-Entity Transformer For Multi-Agent Spatiotemporal Modeling”, 2021-02-05 ( ; backlinks; similar)
“UPDeT: Universal Multi-agent Reinforcement Learning via Policy Decoupling With Transformers”, Et Al 2021
“UPDeT: Universal Multi-agent Reinforcement Learning via Policy Decoupling with Transformers”, 2021-01-20 ( ; similar)
“Imitating Interactive Intelligence”, Et Al 2020
“Imitating Interactive Intelligence”, 2020-12-10 ( ; similar; bibliography)
“TLeague: A Framework for Competitive Self-Play Based Distributed Multi-Agent Reinforcement Learning”, Et Al 2020
“TLeague: A Framework for Competitive Self-Play based Distributed Multi-Agent Reinforcement Learning”, 2020-11-25 ( ; similar; bibliography)
“Towards Playing Full MOBA Games With Deep Reinforcement Learning”, Et Al 2020
“Towards Playing Full MOBA Games with Deep Reinforcement Learning”, 2020-11-25 ( ; similar; bibliography)
“Emergent Road Rules In Multi-Agent Driving Environments”, Et Al 2020
“Emergent Road Rules In Multi-Agent Driving Environments”, 2020-11-21 ( ; similar)
“Reinforcement Learning for Optimization of COVID-19 Mitigation Policies”, Et Al 2020
“Reinforcement Learning for Optimization of COVID-19 Mitigation policies”, 2020-10-20 (backlinks; similar)
“Human-Level Performance in No-Press Diplomacy via Equilibrium Search”, Et Al 2020
“Human-Level Performance in No-Press Diplomacy via Equilibrium Search”, 2020-10-06 ( ; similar)
“Emergent Social Learning via Multi-agent Reinforcement Learning”, Et Al 2020
“Emergent Social Learning via Multi-agent Reinforcement Learning”, 2020-10-01 ( ; similar)
“Grounded Language Learning Fast and Slow”, Et Al 2020
“Grounded Language Learning Fast and Slow”, 2020-09-03 ( ; similar)
“ReBeL: Combining Deep Reinforcement Learning and Search for Imperfect-Information Games”, Et Al 2020
“ReBeL: Combining Deep Reinforcement Learning and Search for Imperfect-Information Games”, 2020-07-27 ( ; similar)
“Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions [blog]”, 2020
“Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions [blog]”, 2020-07-11 (backlinks; similar; bibliography)
“One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control”, Et Al 2020
“One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control”, 2020-07-09 (backlinks; similar)
“Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions”, Et Al 2020
“Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions”, 2020-07-05 ( ; backlinks; similar)
“Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks”, Et Al 2020
“Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks”, 2020-06-14 (backlinks; similar)
“Learning to Play No-Press Diplomacy With Best Response Policy Iteration”, Et Al 2020
“Learning to Play No-Press Diplomacy with Best Response Policy Iteration”, 2020-06-08 ( ; similar)
“Approximate Exploitability: Learning a Best Response in Large Games”, Et Al 2020
“Approximate exploitability: Learning a best response in large games”, 2020-04-20 ( ; similar)
“Real World Games Look Like Spinning Tops”, Et Al 2020
“Real World Games Look Like Spinning Tops”, 2020-04-20 ( ; similar)
“Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and Their Solutions”, Et Al 2020
“Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions”, 2020-03-19 ( ; similar)
“Social Diversity and Social Preferences in Mixed-motive Reinforcement Learning”, Et Al 2020
“Social diversity and social preferences in mixed-motive reinforcement learning”, 2020-02-06 (similar)
“Effective Diversity in Population Based Reinforcement Learning”, Parker-Et Al 2020
“Effective Diversity in Population Based Reinforcement Learning”, 2020-02-03 ( ; similar)
“Towards Learning Multi-agent Negotiations via Self-Play”, 2020
“Towards Learning Multi-agent Negotiations via Self-Play”, 2020-01-28 (similar)
“Smooth Markets: A Basic Mechanism for Organizing Gradient-based Learners”, Et Al 2020
“Smooth markets: A basic mechanism for organizing gradient-based learners”, 2020-01-14 ( ; similar)
“Learning by Cheating”, Et Al 2019
“Learning by Cheating”, 2019-12-27 (backlinks; similar)
“Increasing Generality in Machine Learning through Procedural Content Generation”, 2019
“Increasing Generality in Machine Learning through Procedural Content Generation”, 2019-11-29 ( ; similar)
“Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms”, Et Al 2019
“Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms”, 2019-11-24 ( ; similar)
“Grandmaster Level in StarCraft II Using Multi-agent Reinforcement Learning”, Et Al 2019
“Grandmaster level in StarCraft II using multi-agent reinforcement learning”, 2019-10-30 ( ; similar; bibliography)
“Stabilizing Generative Adversarial Networks: A Survey”, Et Al 2019
“Stabilizing Generative Adversarial Networks: A Survey”, 2019-09-30 ( ; backlinks; similar)
“Emergent Tool Use from Multi-Agent Interaction § Surprising Behavior”, Et Al 2019
“Emergent Tool Use from Multi-Agent Interaction § Surprising behavior”, 2019-09-17 ( ; similar; bibliography)
“Emergent Tool Use From Multi-Agent Autocurricula”, Et Al 2019
“Emergent Tool Use From Multi-Agent Autocurricula”, 2019-09-17 ( ; similar)
“No Press Diplomacy: Modeling Multi-Agent Gameplay”, Et Al 2019
“No Press Diplomacy: Modeling Multi-Agent Gameplay”, 2019-09-04 ( ; similar)
“A Review of Cooperative Multi-Agent Deep Reinforcement Learning”, Oroojlooy2019
“A Review of Cooperative Multi-Agent Deep Reinforcement Learning”, 2019-08-11 (similar)
“Pluribus: Superhuman AI for Multiplayer Poker”, 2019
“Pluribus: Superhuman AI for multiplayer poker”, 2019-07-11 ( ; similar)
“Evolving the Hearthstone Meta”, Et Al 2019
“Evolving the Hearthstone Meta”, 2019-07-02 ( ; similar)
“Evolutionary Implementation of Bayesian Computations”, Et Al 2019
“Evolutionary implementation of Bayesian computations”, 2019-06-28 ( ; backlinks; similar)
“Finding Friend and Foe in Multi-Agent Games”, Et Al 2019
“Finding Friend and Foe in Multi-Agent Games”, 2019-06-05 ( ; similar)
“Hierarchical Decision Making by Generating and Following Natural Language Instructions”, Et Al 2019
“Hierarchical Decision Making by Generating and Following Natural Language Instructions”, 2019-06-03 (similar)
“ICML 2019 Notes”, 2019
“ICML 2019 Notes”, 2019-06 ( ; similar; bibliography)
“Human-level Performance in 3D Multiplayer Games With Population-based Reinforcement Learning”, Et Al 2019
“Human-level performance in 3D multiplayer games with population-based reinforcement learning”, 2019-05-31 ( ; similar; bibliography)
“AI-GAs: AI-generating Algorithms, an Alternate Paradigm for Producing General Artificial Intelligence”, 2019
“AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence”, 2019-05-27 ( ; similar)
“Adversarial Policies: Attacking Deep Reinforcement Learning”, Et Al 2019
“Adversarial Policies: Attacking Deep Reinforcement Learning”, 2019-05-25 ( ; similar)
“Α-Rank: Multi-Agent Evaluation by Evolution”, Et Al 2019
“α-Rank: Multi-Agent Evaluation by Evolution”, 2019-03-04 ( ; similar)
“Autocurricula and the Emergence of Innovation from Social Interaction: A Manifesto for Multi-Agent Intelligence Research”, Et Al 2019
“Autocurricula and the Emergence of Innovation from Social Interaction: A Manifesto for Multi-Agent Intelligence Research”, 2019-03-02 (similar)
“Distilling Policy Distillation”, Et Al 2019
“Distilling Policy Distillation”, 2019-02-06 ( ; similar; bibliography)
“Open-ended Learning in Symmetric Zero-sum Games”, Et Al 2019
“Open-ended Learning in Symmetric Zero-sum Games”, 2019-01-23 (similar)
“Hierarchical Reinforcement Learning for Multi-agent MOBA Game”, Et Al 2019
“Hierarchical Reinforcement Learning for Multi-agent MOBA Game”, 2019-01-23 (similar)
“Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions”, Et Al 2019
“Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions”, 2019-01-07 ( ; similar)
“Hierarchical Macro Strategy Model for MOBA Game AI”, Et Al 2018
“Hierarchical Macro Strategy Model for MOBA Game AI”, 2018-12-19 (similar)
“Continual Match Based Training in Pommerman: Technical Report”, Et Al 2018
“Continual Match Based Training in Pommerman: Technical Report”, 2018-12-18 (similar)
“Malthusian Reinforcement Learning”, Et Al 2018
“Malthusian Reinforcement Learning”, 2018-12-17 ( ; similar)
“Evolution As Backstop for Reinforcement Learning”, 2018
“Evolution as Backstop for Reinforcement Learning”, 2018-12-06 ( ; backlinks; similar; bibliography)
“Stable Opponent Shaping in Differentiable Games”, Et Al 2018
“Stable Opponent Shaping in Differentiable Games”, 2018-11-20 (similar)
“Deep Counterfactual Regret Minimization”, Et Al 2018
“Deep Counterfactual Regret Minimization”, 2018-11-01 ( ; similar)
“TarMAC: Targeted Multi-Agent Communication”, Et Al 2018
“TarMAC: Targeted Multi-Agent Communication”, 2018-10-26 (similar)
“Graph Convolutional Reinforcement Learning”, Et Al 2018
“Graph Convolutional Reinforcement Learning”, 2018-10-22 ( ; similar)
“Social Influence As Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning”, Et Al 2018
“Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning”, 2018-10-19 (similar)
“Deep Reinforcement Learning”, 2018
“Deep Reinforcement Learning”, 2018-10-15 ( ; similar)
“A Survey and Critique of Multiagent Deep Reinforcement Learning”, Hernandez-Et Al 2018
“A Survey and Critique of Multiagent Deep Reinforcement Learning”, 2018-10-12 ( ; similar)
“Learning to Coordinate Multiple Reinforcement Learning Agents for Diverse Query Reformulation”, Et Al 2018
“Learning to Coordinate Multiple Reinforcement Learning Agents for Diverse Query Reformulation”, 2018-09-27 (backlinks; similar)
“Pommerman: A Multi-Agent Playground”, Et Al 2018
“Pommerman: A Multi-Agent Playground”, 2018-09-19 (backlinks; similar)
“Fully Distributed Multi-Robot Collision Avoidance via Deep Reinforcement Learning for Safe and Efficient Navigation in Complex Scenarios”, Et Al 2018
“Fully Distributed Multi-Robot Collision Avoidance via Deep Reinforcement Learning for Safe and Efficient Navigation in Complex Scenarios”, 2018-08-11 ( ; similar)
“Human-level Performance in First-person Multiplayer Games With Population-based Deep Reinforcement Learning”, Et Al 2018
“Human-level performance in first-person multiplayer games with population-based deep reinforcement learning”, 2018-07-03 ( ; similar)
“Construction of Arbitrarily Strong Amplifiers of Natural Selection Using Evolutionary Graph Theory”, Et Al 2018
“Construction of arbitrarily strong amplifiers of natural selection using evolutionary graph theory”, 2018-06-14 ( ; backlinks; similar)
“Adaptive Mechanism Design: Learning to Promote Cooperation”, Et Al 2018
“Adaptive Mechanism Design: Learning to Promote Cooperation”, 2018-06-11 ( ; similar)
“Mix&Match—Agent Curricula for Reinforcement Learning”, Et Al 2018
“Mix&Match—Agent Curricula for Reinforcement Learning”, 2018-06-05 ( ; similar)
“Kickstarting Deep Reinforcement Learning”, Et Al 2018
“Kickstarting Deep Reinforcement Learning”, 2018-03-10 ( ; similar)
“Machine Theory of Mind”, Et Al 2018
“Machine Theory of Mind”, 2018-02-21 ( ; similar)
“Sim-to-Real Optimization of Complex Real World Mobile Network With Imperfect Information via Deep Reinforcement Learning from Self-play”, Et Al 2018
“Sim-to-Real Optimization of Complex Real World Mobile Network with Imperfect Information via Deep Reinforcement Learning from Self-play”, 2018-02-18 ( ; similar)
“Trust-Aware Decision Making for Human-Robot Collaboration: Model Learning and Planning”, Et Al 2018
“Trust-Aware Decision Making for Human-Robot Collaboration: Model Learning and Planning”, 2018-01-12 (similar)
“Emergent Complexity via Multi-Agent Competition”, Et Al 2017
“Emergent Complexity via Multi-Agent Competition”, 2017-10-10 ( ; similar)
“Learning With Opponent-Learning Awareness”, Et Al 2017
“Learning with Opponent-Learning Awareness”, 2017-09-13 ( ; similar)
“LADDER: A Human-Level Bidding Agent for Large-Scale Real-Time Online Auctions”, Et Al 2017
“LADDER: A Human-Level Bidding Agent for Large-Scale Real-Time Online Auctions”, 2017-08-18 ( ; similar)
“CAN: Creative Adversarial Networks, Generating”Art” by Learning About Styles and Deviating from Style Norms”, Et Al 2017
“CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms”, 2017-06-21 ( ; backlinks; similar)
“Supervision via Competition: Robot Adversaries for Learning Tasks”, Et Al 2016
“Supervision via Competition: Robot Adversaries for Learning Tasks”, 2016-10-05 ( ; similar)
“Policy Distillation”, Et Al 2015
“Policy Distillation”, 2015-11-19 ( ; similar)
“Reflective Oracles: A Foundation for Classical Game Theory”, Et Al 2015
“Reflective Oracles: A Foundation for Classical Game Theory”, 2015-08-17 ( ; backlinks; similar)
“One Writer Enters International Competition to Play the World-conquering Game That Redefines What It Means to Be a Geek (and a Person)”
Wikipedia
Miscellaneous
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https://ai.googleblog.com/2019/06/introducing-google-research-football.html
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https://joao-abrantes.com/posts/mimicking-evolution-with-reinforcement-learning/
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https://www.deepmind.com/blog/generally-capable-agents-emerge-from-open-ended-play
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https://www.quantamagazine.org/computers-evolve-a-new-path-toward-human-intelligence-20191106/
Link Bibliography
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https://www.science.org/doi/10.1126/science.ade9097#facebook
: “CICERO: Human-level Play in the Game of <em>Diplomacy< / em> by Combining Language Models With Strategic Reasoning”, : -
https://openreview.net/forum?id=DY1pMrmDkm
: “Modeling Bounded Rationality in Multi-Agent Simulations Using Rationally Inattentive Reinforcement Learning”, Anonymous: -
https://arxiv.org/abs/2208.04024
: “Social Simulacra: Creating Populated Prototypes for Social Computing Systems”, Joon Sung Park, Lindsay Popowski, Carrie J. Cai, Meredith Ringel Morris, Percy Liang, Michael S. Bernstein: -
https://arxiv.org/abs/2206.15378#deepmind
: “DeepNash: Mastering the Game of Stratego With Model-Free Multiagent Reinforcement Learning”, : -
https://arxiv.org/abs/2206.07505
: “Revisiting Some Common Practices in Cooperative Multi-Agent Reinforcement Learning”, Wei Fu, Chao Yu, Zelai Xu, Jiaqi Yang, Yi Wu: -
https://arxiv.org/abs/2205.14953
: “MAT: Multi-Agent Reinforcement Learning Is a Sequence Modeling Problem”, Muning Wen, Jakub Grudzien Kuba, Runji Lin, Weinan Zhang, Ying Wen, Jun Wang, Yaodong Yang: -
https://arxiv.org/abs/2202.07415#deepmind
: “NeuPL: Neural Population Learning”, Siqi Liu, Luke Marris, Daniel Hennes, Josh Merel, Nicolas Heess, Thore Graepel: -
https://arxiv.org/abs/2112.11701#tencent
: “Maximum Entropy Population Based Training for Zero-Shot Human-AI Coordination”, Rui Zhao, Jinming Song, Hu Haifeng, Yang Gao, Yi Wu, Zhongqian Sun, Yang Wei: -
https://arxiv.org/abs/2112.03178#deepmind
: “Player of Games”, : -
https://arxiv.org/abs/2105.12196#deepmind
: “From Motor Control to Team Play in Simulated Humanoid Football”, : -
https://arxiv.org/abs/2104.11980
: “Baller2vec++: A Look-Ahead Multi-Entity Transformer For Modeling Coordinated Agents”, Michael A. Alcorn, Anh Nguyen: -
https://arxiv.org/abs/2012.05672#deepmind
: “Imitating Interactive Intelligence”, : -
https://arxiv.org/abs/2011.12895#tencent
: “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 Zhang: -
https://arxiv.org/abs/2011.12692#tencent
: “Towards Playing Full MOBA Games With Deep Reinforcement Learning”, : -
https://bair.berkeley.edu/blog/2020/07/11/auction/
: “Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions [blog]”, Michael Chang, Sidhant Kaushik: -
2019-vinyals.pdf#deepmind
: “Grandmaster Level in StarCraft II Using Multi-agent Reinforcement Learning”, : -
https://openai.com/blog/emergent-tool-use/#surprisingbehaviors
: “Emergent Tool Use from Multi-Agent Interaction § Surprising Behavior”, Bowen Baker, Ingmar Kanitscheider, Todor Markov, Yi Wu, Glenn Powell, Bob McGrew, Igor Mordatch: -
https://david-abel.github.io/notes/icml_2019.pdf
: “ICML 2019 Notes”, David Abel: -
2019-jaderberg.pdf#deepmind
: “Human-level Performance in 3D Multiplayer Games With Population-based Reinforcement Learning”, : -
https://arxiv.org/abs/1902.02186#deepmind
: “Distilling Policy Distillation”, Wojciech Marian Czarnecki, Razvan Pascanu, Simon Osindero, Siddhant M. Jayakumar, Grzegorz Swirszcz, Max Jaderberg: -
backstop
: “Evolution As Backstop for Reinforcement Learning”, Gwern Branwen: