arXiv:2605.12655v2 Announce Type: replace 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 instruc
The increasing sophistication and integration of AI systems into real-world applications necessitate robust mechanisms for dynamic instruction compliance, particularly in multi-agent environments where human inputs are critical.
This development is crucial for bridging the gap between theoretical multi-agent reinforcement learning and practical, reliable deployment of AI systems that can effectively adapt to human commands, even when conflicting with pre-programmed objectives.
The proposed MAVIC method offers a way to maintain consistent value estimates in MARL when agents receiveinterruptions via natural language, making AI systems more reliable and adaptable in dynamic human-AI interaction scenarios.
- · AI developers
- · Robotics companies
- · Defense sector
- · Logistics and automation
- · Developers of brittle or non-adaptive AI systems
AI agents will become more adept at handling unexpected instructions and adapting their long-term plans in real-time.
This capability could accelerate the deployment of autonomous systems in complex human-centric environments, reducing errors and increasing efficiency.
Improved instruction compliance could lead to more nuanced human-AI collaboration, pushing further into cognitive tasks that require fluid adaptation and understanding of intent.
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