Macro-Action Based Multi-Agent Instruction Following through Value Cancellation

2026-05-15 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

A new paper, "Macro-Action Based Multi-Agent Instruction Following through Value Cancellation" (arXiv:2605.12655), introduces Macro-Action Value Correction for Instruction Compliance (MAVIC), a method to address a fundamental failure mode in multi-agent reinforcement learning (MARL). This issue arises when natural language instructions interrupt ongoing macro-actions, causing Bellman updates to inconsistently couple value estimates across different instruction contexts. MAVIC corrects Bellman backups at instruction boundaries by modifying the bootstrapping target, ensuring consistent value estimation even with stochastic instruction switching within a unified policy. The authors provide theoretical analysis and an actor-critic implementation, demonstrating that MAVIC achieves high instruction compliance while maintaining base task performance in complex cooperative multi-agent environments.

Key takeaway

For research scientists developing multi-agent reinforcement learning systems that must adapt to real-time natural language instructions, you should consider integrating MAVIC. This approach directly addresses value inconsistency issues caused by instruction interruptions, allowing your agents to maintain high instruction compliance and base task performance without resorting to complex reward shaping.

Key insights

MAVIC corrects value estimates in MARL to enable consistent instruction following amidst interruptions.

Principles

• Bellman updates can couple values inconsistently.
• Modify bootstrapping targets, not just rewards.
• Unified policy can handle stochastic instructions.

Method

MAVIC corrects Bellman backups at instruction boundaries by adjusting the incoming instruction objective and restoring the continuation value under the current objective, modifying the bootstrapping target itself.

In practice

• Implement MAVIC in actor-critic MARL systems.
• Apply to environments with dynamic natural language instructions.

Topics

• Multi-Agent Reinforcement Learning
• Instruction Following
• Macro-Actions
• Value Correction
• Bellman Updates

Best for: Research Scientist, AI Scientist, Machine Learning Engineer

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