arXiv:2605.28675v1 Announce Type: new Abstract: Data acquisition efficiency is a central challenge in deploying reinforcement learning in business and healthcare operations, where interactions are costly, slow, and often involve humans in the loop. This paper develops a unified large deviations framework for data acquisition in infinite-horizon reinforcement learning. We introduce the exponential decay rate of the policy-selection error probability as a principled efficiency metric and derive a variational characterization of this rate via large deviations theory for Markov chains, yielding a
The increasing complexity and cost of deploying reinforcement learning models in real-world critical applications necessitate more efficient data acquisition strategies, making this research timely.
Improving data acquisition efficiency is crucial for the practical and economic viability of advanced AI systems, especially in resource-constrained environments like healthcare and business operations.
The theoretical framework presented offers a principled way to optimize data interaction in reinforcement learning, potentially leading to faster and cheaper deployment of AI solutions.
- · AI/ML researchers
- · Healthcare sector (AI applications)
- · Businesses deploying RL
- · Robotics
- · Inefficient RL deployment strategies
- · High-cost data collection methods
More cost-effective and faster development cycles for reinforcement learning applications.
Accelerated adoption of advanced AI in industries where data acquisition was a primary bottleneck.
Enhanced competition among AI developers as the barrier to entry for robust RL systems potentially lowers.
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Read at arXiv cs.LG