arXiv:2606.14095v1 Announce Type: new Abstract: We study the sample complexity of learning in average-reward weakly-coupled Markov decision processes (WCMDPs) and Restless Bandits (RBs) under a generative model. Naive reduction to a tabular MDP leads to high complexity bounds as the state-action space is exponentially large in the number of arms $N$. By exploiting the weakly coupled structure, we show that near-optimal policies can be learned with sample and computational complexities that are polynomial in $N$. Specifically, we analyze the plug-in approach, which applies an efficient planning
This research provides a more efficient approach to learning in complex decision-making systems, addressing current limitations in scaling AI to real-world, high-dimensional problems.
Improved sample complexity in weakly-coupled systems can enable more effective and resource-efficient deployment of AI agents in complex environments, accelerating their practical utility.
The ability to learn near-optimal policies with polynomial complexity rather than exponential complexity fundamentally alters the scalability and feasibility of certain AI applications.
- · AI Developers
- · Robotics
- · Logistics
- · Reinforcement Learning Researchers
- · Inefficient AI deployment strategies
More sophisticated AI agents become viable for deployment in complex, distributed systems.
Reduced computational and data requirements could lower the barrier to entry for developing advanced AI applications in specific domains.
This could contribute to the acceleration of autonomous systems in diverse sectors, enabling new levels of automation and decision-making.
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Read at arXiv cs.LG