New Algorithm BLINQ Learns Whittle Indices More Efficiently Than Q-Learning
Researchers have introduced BLINQ, a model-based algorithm designed to learn Whittle indices for indexable, communicating, and unichain Markov Decision Processes (MDPs). The algorithm builds an empirical estimate of the MDP and computes Whittle indices using an extended state-of-the-art method, with proven convergence guarantees and bounded learning time. Numerical experiments show BLINQ requires fewer samples and lower total computational cost than Q-learning approaches, even those accelerated with neural networks.
BLINQ is a newly proposed model-based reinforcement learning algorithm that learns Whittle indices for a class of MDPs satisfying indexability, communicating, and unichain properties. The approach first constructs an empirical estimate of the underlying MDP from observed data, then applies an extended version of an existing state-of-the-art algorithm to compute the corresponding Whittle indices. The authors provide formal convergence proofs and derive bounds on the time required to achieve learning with arbitrary precision, alongside an analysis of computational complexity. In numerical experiments, BLINQ substantially outperformed existing Q-learning baselines in sample efficiency — the number of observations needed to reach accurate approximations. Notably, BLINQ also achieved lower total computational cost than Q-learning for any reasonably large sample count, a result that held even when Q-learning was augmented with neural networks for Q-value prediction. The paper, spanning 30 pages with 7 figures, has been submitted to the ACM Transactions on Modeling and Performance Evaluation of Computing Systems (TOMPECS).
What's missing
The algorithm's performance guarantees are restricted to MDPs satisfying specific structural assumptions (indexability, communicating, unichain), and it is unclear how BLINQ generalizes beyond these settings. The tightness of the derived sample complexity bounds relative to known lower bounds is not discussed in the abstract. Real-world applicability to large-scale or continuous-state MDPs, and sensitivity to misspecification of the MDP model, remain open questions.
What different sources said
- arXiv cs.LGCenter
Model-Based Learning of Whittle indices
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