arXiv:2511.20397v2 Announce Type: replace Abstract: We present BLINQ, a new model-based algorithm that learns the Whittle indices of an indexable, communicating and unichain Markov Decision Process (MDP). Our approach relies on building an empirical estimate of the MDP and then computing its Whittle indices using an extended version of a state-of-the-art existing algorithm. We provide a proof of convergence to the Whittle indices we want to learn as well as a bound on the time needed to learn them with arbitrary precision. Moreover, we investigate its computational complexity. Our numerical ex
The continuous advancements in AI research, particularly in reinforcement learning and decision-making algorithms, are leading to more sophisticated methods for optimizing complex systems.
This research provides a more efficient and accurate way to learn optimal policies for dynamic systems, which is crucial for applications in resource allocation and autonomous agents.
The ability to accurately and efficiently learn Whittle indices for complex Markov Decision Processes will enable more robust and adaptive AI systems in real-world scenarios.
- · AI researchers
- · Developers of autonomous systems
- · Logistics and resource management sectors
- · Systems relying on heuristic allocation methods
- · Less efficient model-free reinforcement learning approaches
Improved performance and efficiency in multi-agent reinforcement learning environments where resources need optimal allocation.
Accelerated development of AI agents capable of making complex, strategic decisions in dynamic and uncertain settings.
Enhanced automation across various industries due to more reliable and intelligent decision-making systems, potentially impacting labor markets.
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