Macro-Action Based Multi-Agent Instruction Following through Value Cancellation
Multi-agent reinforcement learning (MARL) in real-world scenarios often needs to follow external natural language instructions that may interrupt ongoing macro-actions and conflict with long-term objectives. This paper introduces MAVIC, which corrects Bellman backups at instruction boundaries to ensure consistent value estimation. Experiments show MAVIC achieves high instruction compliance while preserving base task performance in complex cooperative environments.
[2605.12655] Macro-Action Based Multi-Agent Instruction Following through Value Cancellation
[Submitted on 12 May 2026]
Title:Macro-Action Based Multi-Agent Instruction Following through Value Cancellation
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Abstract:Multi-agent reinforcement learning (MARL) in real-world use cases may need to adapt to external natural language instructions that interrupt ongoing behavior and conflict with long-horizon objectives. However, conditioning rewards on instructions introduces a fundamental failure mode as Bellman updates couple value estimates across instruction contexts, leading to inconsistent values when instructions interrupt macro-actions. We propose Macro-Action Value Correction for Instruction Compliance (MAVIC), which corrects Bellman backups at instruction boundaries by correcting the incoming instruction objective and restoring the continuation value under the current objective. Unlike reward shaping, MAVIC modifies the bootstrapping target itself, enabling consistent value estimation under stochastic instruction switching within a unified policy. We provide theoretical analysis and an actor-critic implementation, and show that MAVIC achieves high instruction compliance while preserving base task performance in increasingly complex cooperative multi-agent environments.
Subjects:
Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
Cite as: arXiv:2605.12655 [cs.AI]
(or arXiv:2605.12655v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.12655
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Wo Wei Lin [view email] [v1] Tue, 12 May 2026 19:01:16 UTC (356 KB)
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