Learning When to Cooperate Under Heterogeneous Goals

2026-03-10 · Source: cs.MA updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Advanced, extended

Summary

This research introduces a novel approach to Ad Hoc Teamwork (AHT) that addresses scenarios where agents have heterogeneous goals, which may or may not overlap. The study extends the typical AHT setting, which often assumes universal cooperation, to better reflect real-world collaborative environments. The authors propose GRILL (Goal selection by RL with Imitation for Low-Level control), a hierarchical method combining imitation learning for low-level action selection and reinforcement learning for high-level goal selection. GRILL and its variant, GRILL-M (which includes an auxiliary teammate modeling component), were evaluated on extended versions of two cooperative gridworld environments: Cooperative Reaching and Level-Based Foraging. The results demonstrate that GRILL and GRILL-M consistently outperform baseline methods like PPO, LIAM, and OMG, achieving higher average returns and exhibiting more flexible goal selection across full-overlap, partial-overlap, and no-overlap scenarios. GRILL-M's performance advantage over GRILL increased significantly with noisier teammate goal information.

Key takeaway

For research scientists developing multi-agent systems, understanding when to pursue collaborative versus independent goals is critical for real-world performance. You should consider hierarchical learning approaches like GRILL that explicitly separate goal selection from low-level action control, especially in environments with heterogeneous agent objectives. Integrating auxiliary teammate modeling can further enhance performance when teammate goal information is uncertain or noisy.

Key insights

Agents with heterogeneous goals benefit from learning when to cooperate versus act independently.

Principles

• Optimal low-level policies are universal across agents.
• Optimal high-level policies depend on ego and teammate goals.

Method

GRILL uses a two-stage hierarchical process: offline imitation learning for a universal low-level goal-conditioned action policy, and online PPO for a high-level goal selection policy.

In practice

• Separate goal selection from action execution.
• Use auxiliary teammate modeling for noisy goal information.

Topics

• Ad Hoc Teamwork
• Hierarchical Reinforcement Learning
• Heterogeneous Goals
• Imitation Learning
• Teammate Modeling

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