“‘MARL’ Tag”,2019-12-17 (AI scaling, mechanism design, RL exploration, AI safety, RL scaling)⁠:

Bibliography for tag reinforcement-learning/multi-agent, most recent first: 12 related tags, 168 annotations, & 27 links (parent).

• See Also
• Gwern
  • “Evolution As Backstop for Reinforcement Learning”, Gwern2018
  • “Fashion Cycles”, Gwern2021
• Links
  • “On Scalable Oversight With Weak LLMs Judging Strong LLMs”, Kenton et al 2024
  • “Foundational Challenges in Assuring Alignment and Safety of Large Language Models”, Anwar et al 2024
  • “From Reinforcement Learning to Agency: Frameworks for Understanding Basal Cognition”, Seifert et al 2024
  • “Classical Sorting Algorithms As a Model of Morphogenesis: Self-Sorting Arrays Reveal Unexpected Competencies in a Minimal Model of Basal Intelligence”, Zhang et al 2023
  • “PRER: Modeling Complex Mathematical Reasoning via Large Language Model Based MathAgent”, Liao et al 2023
  • “Generative Agent-Based Modeling With Actions Grounded in Physical, Social, or Digital Space Using Concordia”, Vezhnevets et al 2023
  • “Learning Few-Shot Imitation As Cultural Transmission”, Bhoopchand et al 2023
  • “JaxMARL: Multi-Agent RL Environments in JAX”, Rutherford et al 2023
  • “Large Language Models Can Strategically Deceive Their Users When Put Under Pressure”, Scheurer et al 2023
  • “Neural MMO 2.0: A Massively Multi-Task Addition to Massively Multi-Agent Learning”, Suárez et al 2023
  • “Let Models Speak Ciphers: Multiagent Debate through Embeddings”, Pham et al 2023
  • “AI Deception: A Survey of Examples, Risks, and Potential Solutions”, Park et al 2023
  • “Diversifying AI: Towards Creative Chess With AlphaZero (AZ_db)”, Zahavy et al 2023
  • “Hoodwinked: Deception and Cooperation in a Text-Based Game for Language Models”, O’Gara2023
  • “Combining Human Expertise With Artificial Intelligence: Experimental Evidence from Radiology”, Agarwal et al 2023
  • “Posterior Sampling for Multi-Agent Reinforcement Learning: Solving Extensive Games With Imperfect Information”, Zhou et al 2023
  • “Reinforcement Learning in Newcomb-Like Environments”, Bell et al 2023
  • “Learning Agile Soccer Skills for a Bipedal Robot With Deep Reinforcement Learning”, Haarnoja et al 2023
  • “Multi-Party Chat (MultiLIGHT): Conversational Agents in Group Settings With Humans and Models”, Wei et al 2023
  • “Off-The-Grid MARL (OG-MARL): Datasets With Baselines for Offline Multi-Agent Reinforcement Learning”, Formanek et al 2023
  • “Learning to Control and Coordinate Mixed Traffic Through Robot Vehicles at Complex and Unsignalized Intersections”, Wang et al 2023
  • “Melting Pot 2.0”, Agapiou et al 2022
  • “CICERO: Human-Level Play in the Game of Diplomacy by Combining Language Models With Strategic Reasoning”, Bakhtin et al 2022
  • “Over-Communicate No More: Situated RL Agents Learn Concise Communication Protocols”, Kalinowska et al 2022
  • “Human-AI Coordination via Human-Regularized Search and Learning”, Hu et al 2022
  • “Game Theoretic Rating in N-Player General-Sum Games With Equilibria”, Marris et al 2022
  • “Modeling Bounded Rationality in Multi-Agent Simulations Using Rationally Inattentive Reinforcement Learning”, Anonymous2022
  • “Neural Payoff Machines: Predicting Fair and Stable Payoff Allocations Among Team Members”, Cornelisse et al 2022
  • “Social Simulacra: Creating Populated Prototypes for Social Computing Systems”, Park et al 2022
  • “DeepNash: Mastering the Game of Stratego With Model-Free Multiagent Reinforcement Learning”, Perolat et al 2022
  • “Fleet-DAgger: Interactive Robot Fleet Learning With Scalable Human Supervision”, Hoque et al 2022
  • “Revisiting Some Common Practices in Cooperative Multi-Agent Reinforcement Learning”, Fu et al 2022
  • “MAT: Multi-Agent Reinforcement Learning Is a Sequence Modeling Problem”, Wen et al 2022
  • “First Contact: Unsupervised Human-Machine Co-Adaptation via Mutual Information Maximization”, Reddy et al 2022
  • “Emergent Bartering Behavior in Multi-Agent Reinforcement Learning”, Johanson et al 2022
  • “NeuPL: Neural Population Learning”, Liu et al 2022
  • “Uncalibrated Models Can Improve Human-AI Collaboration”, Vodrahalli et al 2022
  • “Human-Centered Mechanism Design With Democratic AI”, Koster et al 2022
  • “Hidden Agenda: a Social Deduction Game With Diverse Learned Equilibria”, Kopparapu et al 2022
  • “Finding General Equilibria in Many-Agent Economic Simulations Using Deep Reinforcement Learning”, Curry et al 2022
  • “Maximum Entropy Population Based Training for Zero-Shot Human-AI Coordination”, Zhao et al 2021
  • “Modeling Strong and Human-Like Gameplay With KL-Regularized Search”, Jacob et al 2021
  • “Offline Pre-Trained Multi-Agent Decision Transformer: One Big Sequence Model Tackles All SMAC Tasks”, Meng et al 2021
  • “Player of Games”, Schmid et al 2021
  • “Collective Intelligence for Deep Learning: A Survey of Recent Developments”, Ha & Tang2021
  • “Learning to Ground Multi-Agent Communication With Autoencoders”, Lin et al 2021
  • “Meta-Learning, Social Cognition and Consciousness in Brains and Machines”, Langdon et al 2021
  • “Collaborating With Humans without Human Data”, Strouse et al 2021
  • “The Neural MMO Platform for Massively Multiagent Research”, Suarez et al 2021
  • “Replay-Guided Adversarial Environment Design”, Jiang et al 2021
  • “DORA: No-Press Diplomacy from Scratch”, Bakhtin et al 2021
  • “Embodied Intelligence via Learning and Evolution”, Gupta et al 2021
  • “Trust Region Policy Optimization in Multi-Agent Reinforcement Learning”, Kuba et al 2021
  • “WarpDrive: Extremely Fast End-To-End Deep Multi-Agent Reinforcement Learning on a GPU”, Lan et al 2021
  • “The AI Economist: Optimal Economic Policy Design via Two-Level Deep Reinforcement Learning”, Zheng et al 2021
  • “Open-Ended Learning Leads to Generally Capable Agents”, Team et al 2021
  • “Megaverse: Simulating Embodied Agents at One Million Experiences per Second”, Petrenko et al 2021
  • “Scalable Evaluation of Multi-Agent Reinforcement Learning With Melting Pot”, Leibo et al 2021
  • “From Motor Control to Team Play in Simulated Humanoid Football”, Liu et al 2021
  • “Cooperative AI Foundation (CAIF)”, CAIF 2021
  • “Baller2vec++: A Look-Ahead Multi-Entity Transformer For Modeling Coordinated Agents”, Alcorn & Nguyen2021
  • “Neural Tree Expansion for Multi-Robot Planning in Non-Cooperative Environments”, Riviere et al 2021
  • “Multitasking Inhibits Semantic Drift”, Jacob et al 2021
  • “Asymmetric Self-Play for Automatic Goal Discovery in Robotic Manipulation”, OpenAI et al 2021
  • “Reinforcement Learning for Datacenter Congestion Control”, Tessler et al 2021
  • “Baller2vec: A Multi-Entity Transformer For Multi-Agent Spatiotemporal Modeling”, Alcorn & Nguyen2021
  • “UPDeT: Universal Multi-Agent Reinforcement Learning via Policy Decoupling With Transformers”, Hu et al 2021
  • “Imitating Interactive Intelligence”, Abramson et al 2020
  • “Towards Playing Full MOBA Games With Deep Reinforcement Learning”, Ye et al 2020
  • “TLeague: A Framework for Competitive Self-Play Based Distributed Multi-Agent Reinforcement Learning”, Sun et al 2020
  • “Emergent Road Rules In Multi-Agent Driving Environments”, Pal et al 2020
  • “Reinforcement Learning for Optimization of COVID-19 Mitigation Policies”, Kompella et al 2020
  • “Human-Level Performance in No-Press Diplomacy via Equilibrium Search”, Gray et al 2020
  • “Emergent Social Learning via Multi-Agent Reinforcement Learning”, Ndousse et al 2020
  • “Grounded Language Learning Fast and Slow”, Hill et al 2020
  • “ReBeL: Combining Deep Reinforcement Learning and Search for Imperfect-Information Games”, Brown et al 2020
  • “Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions [Blog]”, Chang & Kaushik2020
  • “One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control”, Huang et al 2020
  • “Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions”, Chang et al 2020
  • “Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks”, Papoudakis et al 2020
  • “Learning to Play No-Press Diplomacy With Best Response Policy Iteration”, Anthony et al 2020
  • “Real World Games Look Like Spinning Tops”, Czarnecki et al 2020
  • “Approximate Exploitability: Learning a Best Response in Large Games”, Timbers et al 2020
  • “Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and Their Solutions”, Wang et al 2020
  • “Social Diversity and Social Preferences in Mixed-Motive Reinforcement Learning”, McKee et al 2020
  • “Effective Diversity in Population Based Reinforcement Learning”, Parker-Holder et al 2020
  • “Towards Learning Multi-Agent Negotiations via Self-Play”, Tang2020
  • “Smooth Markets: A Basic Mechanism for Organizing Gradient-Based Learners”, Balduzzi et al 2020
  • “MicrobatchGAN: Stimulating Diversity With Multi-Adversarial Discrimination”, Mordido et al 2020
  • “Learning by Cheating”, Chen et al 2019
  • “Increasing Generality in Machine Learning through Procedural Content Generation”, Risi & Togelius2019
  • “Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms”, Zhang et al 2019
  • “Grandmaster Level in StarCraft II Using Multi-Agent Reinforcement Learning”, Vinyals et al 2019
  • “Multiplayer AlphaZero”, Petosa & Balch2019
  • “Stabilizing Generative Adversarial Networks: A Survey”, Wiatrak et al 2019
  • “Emergent Tool Use From Multi-Agent Autocurricula”, Baker et al 2019
  • “Emergent Tool Use from Multi-Agent Interaction § Surprising Behavior”, Baker et al 2019
  • “Emergent Tool Use from Multi-Agent Interaction § Surprising Behavior”, Baker et al 2019
  • “No Press Diplomacy: Modeling Multi-Agent Gameplay”, Paquette et al 2019
  • “A Review of Cooperative Multi-Agent Deep Reinforcement Learning”, OroojlooyJadid & Hajinezhad2019
  • “Pluribus: Superhuman AI for Multiplayer Poker”, Brown & Sandholm2019
  • “Evolving the Hearthstone Meta”, Silva et al 2019
  • “Evolutionary Implementation of Bayesian Computations”, Czégel et al 2019
  • “Finding Friend and Foe in Multi-Agent Games”, Serrino et al 2019
  • “Hierarchical Decision Making by Generating and Following Natural Language Instructions”, Hu et al 2019
  • “ICML 2019 Notes”, Abel2019
  • “Human-Level Performance in 3D Multiplayer Games With Population-Based Reinforcement Learning”, Jaderberg et al 2019
  • “AI-GAs: AI-Generating Algorithms, an Alternate Paradigm for Producing General Artificial Intelligence”, Clune2019
  • “Adversarial Policies: Attacking Deep Reinforcement Learning”, Gleave et al 2019
  • “LIGHT: Learning to Speak and Act in a Fantasy Text Adventure Game”, Urbanek et al 2019
  • “Α-Rank: Multi-Agent Evaluation by Evolution”, Omidshafiei et al 2019
  • “Autocurricula and the Emergence of Innovation from Social Interaction: A Manifesto for Multi-Agent Intelligence Research”, Leibo et al 2019
  • “Distilling Policy Distillation”, Czarnecki et al 2019
  • “Hierarchical Reinforcement Learning for Multi-Agent MOBA Game”, Zhang et al 2019
  • “Open-Ended Learning in Symmetric Zero-Sum Games”, Balduzzi et al 2019
  • “Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions”, Wang et al 2019
  • “Hierarchical Macro Strategy Model for MOBA Game AI”, Wu et al 2018
  • “Continual Match Based Training in Pommerman: Technical Report”, Peng et al 2018
  • “Malthusian Reinforcement Learning”, Leibo et al 2018
  • “Stable Opponent Shaping in Differentiable Games”, Letcher et al 2018
  • “Deep Counterfactual Regret Minimization”, Brown et al 2018
  • “TarMAC: Targeted Multi-Agent Communication”, Das et al 2018
  • “Graph Convolutional Reinforcement Learning”, Jiang et al 2018
  • “Social Influence As Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning”, Jaques et al 2018
  • “Deep Reinforcement Learning”, Li2018
  • “A Survey and Critique of Multiagent Deep Reinforcement Learning”, Hernandez-Leal et al 2018
  • “Learning to Coordinate Multiple Reinforcement Learning Agents for Diverse Query Reformulation”, Nogueira et al 2018
  • “Pommerman: A Multi-Agent Playground”, Resnick et al 2018
  • “Fully Distributed Multi-Robot Collision Avoidance via Deep Reinforcement Learning for Safe and Efficient Navigation in Complex Scenarios”, Fan et al 2018
  • “Human-Level Performance in First-Person Multiplayer Games With Population-Based Deep Reinforcement Learning”, Jaderberg et al 2018
  • “Construction of Arbitrarily Strong Amplifiers of Natural Selection Using Evolutionary Graph Theory”, Pavlogiannis et al 2018
  • “Adaptive Mechanism Design: Learning to Promote Cooperation”, Baumann et al 2018
  • “Mix&Match—Agent Curricula for Reinforcement Learning”, Czarnecki et al 2018
  • “Kickstarting Deep Reinforcement Learning”, Schmitt et al 2018
  • “Machine Theory of Mind”, Rabinowitz et al 2018
  • “Sim-To-Real Optimization of Complex Real World Mobile Network With Imperfect Information via Deep Reinforcement Learning from Self-Play”, Tan et al 2018
  • “Trust-Aware Decision Making for Human-Robot Collaboration: Model Learning and Planning”, Chen et al 2018
  • “Emergent Complexity via Multi-Agent Competition”, Bansal et al 2017
  • “Learning With Opponent-Learning Awareness”, Foerster et al 2017
  • “LADDER: A Human-Level Bidding Agent for Large-Scale Real-Time Online Auctions”, Wang et al 2017
  • “CAN: Creative Adversarial Networks, Generating “Art” by Learning About Styles and Deviating from Style Norms”, Elgammal et al 2017
  • “On Convergence and Stability of GANs”, Kodali et al 2017
  • “Supervision via Competition: Robot Adversaries for Learning Tasks”, Pinto et al 2016
  • “Policy Distillation”, Rusu et al 2015
  • “Reflective Oracles: A Foundation for Classical Game Theory”, Fallenstein et al 2015
  • “Homo Moralis-Preference Evolution Under Incomplete Information and Assortative Matching”, Alger & Weibull2013
  • “A Self-Coordinating Bus Route to Resist Bus Bunching”, III & Eisenstein2012
  • “Language Evolution in the Laboratory”, Scott-Phillips & Kirby2010
  • “If Multi-Agent Learning Is the Answer, What Is the Question?”, Shoham et al 2007
  • “Market-Based Reinforcement Learning in Partially Observable Worlds”, Kwee et al 2001
  • “Properties of the Bucket Brigade Algorithm”, Holland1985
  • “Computer-Aided Gas Pipeline Operation Using Genetic Algorithms And Rule Learning”, Goldberg1983
  • “Collaborating With Humans Requires Understanding Them”
  • “The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games”
  • “Efficient Large-Scale Fleet Management via Multi-Agent Deep Reinforcement Learning”
  • “Generally Capable Agents Emerge from Open-Ended Play”
  • “One Writer Enters International Competition to Play the World-Conquering Game That Redefines What It Means to Be a Geek (and a Person)”
  • “Mimicking Evolution With Reinforcement Learning”
  • “LLM Powered Autonomous Agents”
  • “The Pommerman Team Competition Or: How We Learned to Stop Worrying and Love the Battle”
  • “New Winning Strategies for the Iterated Prisoner’s Dilemma”
  • “How DeepMind’s Generally Capable Agents Were Trained”
  • “How Much Compute Was Used to Train DeepMind’s Generally Capable Agents?”
  • “DeepMind: Generally Capable Agents Emerge from Open-Ended Play”
  • “So Has AI Conquered Bridge?”
  • “The Steely, Headless King of Texas Hold ’Em”
  • “Artificial Intelligence Beats Eight World Champions at Bridge”
  • “Open-Ended Learning Leads to Generally Capable Agents [Video]”
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