Unsupervised Partner Design Enables Robust Ad-hoc Teamwork
We introduce Unsupervised Partner Design (UPD), a population-free multi-agent reinforcement learning method for robust ad-hoc teamwork. UPD generates training partners on-the-fly and selects them adaptively based on a learnability criterion, removing the need for pre-trained partner populations or manual parameter tuning. We show that this simple mechanism enables effective partner diversity and can be extended to joint partner-environment selection when a procedural level generator is available. Across Level-Based Foraging, Overcooked-AI, and the Overcooked Generalisation Challenge, UPD consistently achieves strong performance compared to both population-based and population-free baselines. In a human-AI user study, agents trained with UPD achieve higher returns and are rated as more adaptive, more human-like, and less frustrating than all evaluated baseline methods.
Code (0)
등록된 구현이 없습니다.
Tasks
Multi-agent Reinforcement LearningSimilar Papers 제목 키워드 기반
A Minimax Approach to Ad Hoc Teamwork
We propose a minimax-Bayes approach to Ad Hoc Teamwork (AHT) that optimizes policies against an adversarial prior over partners, explicitly accounting for uncertainty about partners at time of deployment. Unlike existing…
ROTATE: Regret-driven Open-ended Training for Ad Hoc Teamwork
Developing AI agents capable of collaborating with previously unseen partners is a fundamental generalization challenge in multi-agent learning, known as Ad Hoc Teamwork (AHT). Existing AHT approaches typically adopt a t…
Improving Human-Robot Teamwork in Urban Search and Rescue Through Episodic Memory of Prior Collaboration
Effective human-robot teamwork requires robots to adapt to partners, situations, and task dynamics from the start of an interaction. In the MATRX Urban Search and Rescue (USAR) environment, people can externalize collabo…
Graph Representation LearningMinimum Coverage Sets for Training Robust Ad Hoc Teamwork Agents
Robustly cooperating with unseen agents and human partners presents significant challenges due to the diverse cooperative conventions these partners may adopt. Existing Ad Hoc Teamwork (AHT) methods address this challeng…
DiversityModeling Latent Partner Strategies for Adaptive Zero-Shot Human-Agent Collaboration
In collaborative tasks, being able to adapt to your teammates is a necessary requirement for success. When teammates are heterogeneous, such as in human-agent teams, agents need to be able to observe, recognize, and adap…