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SpecMAGIC: Specification-Guided Multi-Task Generalization in Multi-Agent Reinforcement Learning

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Briefings

SpecMAGIC is a hierarchical framework for multi-agent reinforcement learning that uses a specification language combining temporal logic with operators to decompose complex tasks into reusable subtasks. A centralized planner allocates compatible subtask sequences across agents, enabling zero-shot generalization to unseen tasks without retraining. Experiments on multi-agent navigation tasks demonstrate that SpecMAGIC substantially outperforms existing approaches on unseen specifications and maintains strong performance as the number of agents increases.

Key points

• SpecMAGIC introduces a hierarchical framework using a specification language that combines temporal-logic task specifications with operators for expressing requirements.
• A centralized high-level planner identifies and allocates compatible subtask sequences across agents, while decentralized low-level policies execute individual subtasks.

Context

Multi-task generalization is a critical challenge in multi-agent reinforcement learning (MARL) for real-world applications requiring diverse inter-agent coordination. SpecMAGIC addresses this by enabling zero-shot generalization to new tasks via reusable subtask policies, significantly outperforming existing methods on unseen specifications.

Limitations

Evidence is limited to a single preprint (arXiv:2610.30443v1) from October 2026. Results are based on multi-agent navigation tasks and do not include performance data for other task types or agent counts beyond the tested range.

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