arXiv:2511.02304v2 Announce Type: replace-cross Abstract: We study learning multi-task, multi-agent policies for cooperative, temporal objectives, under centralized training, decentralized execution. In this setting, using automata to represent tasks assigned to agents enables breaking down a team-level objective into simpler, smaller sub-tasks. However, existing approaches remain sample-inefficient and are limited to the single-task case, requiring retraining policies for each new task. In this work, we present Automata-Conditioned Cooperative Multi-Agent Reinforcement Learning (ACC-MARL), a
The increasing complexity of multi-agent systems and the demand for more efficient and adaptable AI have pushed research towards novel approaches that handle temporal objectives and sample efficiency.
This development in multi-agent reinforcement learning directly addresses the challenge of creating more generalizable and less resource-intensive AI agents, which is critical for scaling autonomous systems.
The introduction of Automata-Conditioned Cooperative Multi-Agent Reinforcement Learning (ACC-MARL) offers a method to break down complex team-level objectives into simpler sub-tasks, improving efficiency and adaptability over existing single-task approaches.
- · AI developers
- · Robotics companies
- · Logistics and supply chain sector
- · Defense contractors
- · Companies reliant on single-task AI solutions
- · Inefficient multi-agent system developers
More sophisticated and adaptive multi-agent AI systems become viable across various industries.
Reduced development costs and accelerated deployment of complex autonomous applications in real-world environments.
Enhanced automation capabilities lead to significant shifts in workforce structure and increased demand for AI-literate professionals.
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