Zero-Shot Coordination

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11 papers4 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

PopGen: Generating Semantically Diverse Populations for Zero-Shot Coordination
Oct 11, 2026Paul Teiletche, Kanghyun Ryu, Kyungjoon Park +3Zero-Shot Coordination Multi-Agent Coordination
EPFL · UC Berkeley · Yonsei University +2
Partially Observable Zero-shot coordination by Predicting Intention of Partner
Oct 6, 2026Jinnyeong Yang, Yuhwan Jeong, Hoyong Kwon +3Multi-Agent Coordination Partially Observable RL
KAIST, Visual Intelligence Lab
Grounded Joint-Attention Other-Play for Zero-Shot Coordination
Oct 5, 2026Giulia Benintendi, Constantin Ruhdorfer, Fabian Kögel +1Multi-Agent Coordination Visual Attention
University of Zurich · University of Stuttgart
Watch, Infer, Coordinate: Inferring Robot Partner Constraints for Zero-Shot Coordination
Oct 1, 2026Suyu Ye, Zheyuan Zhang, Vaishnav Tadiparthi +5Multi-Agent Coordination Multi-Robot Coordination
Honda Research Institute USA · Johns Hopkins University
Structured LLM Reasoning for Zero-Shot Human--Robot Coordination Under Hidden Goals
Aug 5, 2026Dong Hae Mangalindan, Anand Gokhale, Francesco Bullo +1Large Language Model-Based Robot Planning Human-Robot Collaboration
Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824 · Center for Control, Dynamical Systems, and Computation, UC Santa Barbara, Santa Barbara, CA 93106 USA
Is Inter-Seed Cross-Play Enough? Evaluating the Robustness of Zero-Shot Coordination Algorithms to Implementation Details
Aug 4, 2026Maksymilian Wolski, Nicholas Hoernle, Johannes Forkel +1Multi-Agent Coordination AI Agent Evaluation
ProrokLab University of Cambridge United Kingdom · QuantCo · BOLD† University of Oxford United Kingdom
Benchmarking Open-Ended Multi-Agent Coordination in Language Agents
Jun 6, 2026Kale-ab Abebe Tessera, Andras Szecsenyi, Cameron Barker +7Multi-Agent Coordination LLM Agent Evaluation
University of Edinburgh · University of Oxford · University College London
Beyond Partner Diversity: An Influence-Based Team Steering Framework for Zero-Shot Human-Machine Teaming
May 14, 2026Wei Sheng, Rohan PalejaMulti-Agent Coordination Cooperative MARL
Department of Computer Science Purdue University
Shaping Zero-Shot Coordination via State Blocking
May 12, 2026Mingu Kang, Sunwoo Lee, Yonghyeon Jo +1Multi-Agent Coordination Cooperative MARL
Graduate School of Artificial Intelligence UNIST Ulsan, South Korea 44919
Zero Shot Coordination for Sparse Reward Tasks with Diverse Reward Shapings
Apr 28, 2026Keenan Powell, Peihong Yu, Pratap TokekarMulti-Agent Coordination Reward Shaping
Department of Computer Science University of Maryland, College Park
Training Generalizable Collaborative Agents via Strategic Risk Aversion
Feb 25, 2026Chengrui Qu, Yizhou Zhang, Nicolas Lanzetti +1Zero-Shot Coordination Cooperative MARL
Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA.

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PopGen: Generating Semantically Diverse Populations for Zero-Shot Coordination

Paul Teiletche, Kanghyun Ryu, Kyungjoon Park, Dhruva Tirumala+2

EPFL · UC Berkeley · Yonsei University+2

Zero-Shot Coordination Multi-Agent Coordination Cooperative MARL

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In multi-agent reinforcement learning, exposing a cooperator to partners with diverse strategies during training is key to better collaboration at test time. However, existing methods typically rely on trajectory-level proxies to measure diversity, which may label two distinct trajectories of the same strategy as diverse. Instead, our key insight is to leverage LLMs' ability to describe behaviorally distinct agents in environments, similar to how humans can recognize high-level strategies. To this end, we introduce PopGen, a framework that synthesizes semantically diverse agents from scratch using LLMs. PopGen first generates a diverse set of textual descriptions of agent behaviors, then translates each behavior into a code policy that can be executed within the environment. We show that cooperators trained against PopGen populations achieve robust zero-shot cooperation with unseen partners, outperforming population-play baselines across four collaborative environments. We further show that semantic diversity scales with PopGen population size, improving the cooperator's worst-case performance, and we identify design choices that preserve population quality while maximizing diversity. Our code is available at https://github.com/labicon/PopGen.