Theory of Mind Guided Strategy Adaptation for Zero-Shot Coordination

2026-02-16 · Source: cs.MA updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

A new framework, Theory of Mind-based Best Response Selection (TBS), addresses the challenge of zero-shot coordination (ZSC) in multi-agent reinforcement learning by enabling agents to adapt to unseen teammates. Unlike traditional population-based approaches that train a single, static best-response policy, TBS employs an adaptive ensemble agent. This agent first infers a teammate's intentions using Theory of Mind (ToM) models and then selects the most suitable policy from a pre-trained ensemble of specialized best-response policies. Experiments conducted in the Overcooked environment, across seven distinct layouts and both fully and partially observable settings, demonstrate that TBS consistently outperforms a single best-response baseline. The method's performance gap over baselines increases with the size of the training partner pool, highlighting its superior adaptability.

Key takeaway

For research scientists developing multi-agent systems requiring robust zero-shot coordination, consider integrating Theory of Mind-based adaptive policy selection. Your systems can achieve superior performance and adaptability by clustering partner strategies and dynamically selecting specialized best-response policies, especially as partner diversity increases. This approach mitigates the limitations of static generalist policies, leading to more effective collaboration with unseen agents in complex environments like Overcooked.

Key insights

Theory of Mind-guided policy selection enhances zero-shot coordination by enabling adaptive responses to diverse, unseen teammates.

Principles

• Adaptive policies outperform static generalists in diverse multi-agent settings.
• Clustering partner behaviors improves computational efficiency and generalization.
• Explicit intention inference (ToM) enhances coordination with novel partners.

Method

TBS constructs a diverse partner pool, clusters partners into behavioral groups via self-tuning spectral clustering, trains specialized best-response policies for each cluster, and uses ToM models to infer partner intent for real-time policy selection.

In practice

• Use self-tuning spectral clustering for automatic strategy grouping.
• Implement recurrent ToM networks to predict partner intentions.
• Train specialized policies for distinct behavioral clusters.

Topics

• Zero-shot Coordination
• Theory of Mind
• Multi-Agent Reinforcement Learning
• Best-Response Policies
• Spectral Clustering

Code references

• andrewni2002/ToMZSC

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.MA updates on arXiv.org.