@article{ValkoKudenko2025,
Author = {Danila Valko and Daniel Kudenko},
Title = {Hybrid pathfinding optimization for the Lightning Network with Reinforcement Learning},

Journal = {Engineering Applications of Artificial Intelligence},
Year = {2025},
Pages = {110225},
chapter = {146},
Doi = {10.1016/j.engappai.2025.110225},
type = {article},
Abstract = {Payment channel networks, such as Bitcoin’s Lightning Network, have emerged to address blockchain scalability issues, enabling rapid transactions. Despite their potential, these networks often experience payment failures due to delays in pathfinding, unreliable routes and infrastructure issues, resulting in excessive carbon emissions. Current reinforcement learning solutions for payment channel networks mainly address issues like payment channel balance and routing fees but often overlook the infrastructure-related causes of payment failure. This paper introduces a novel reinforcement learning-based architecture that combines reinforcement learning agent with native deterministic pathfinding algorithms. This hybrid approach leverages the fast, complete solutions of deterministic algorithms while adapting to the network’s dynamic and probabilistic nature of payments to significantly enhance payment success rates. Experiments on real network snapshots show that this approach outperforms native pathfinding algorithms and state-of-the-art static optimization methods, providing improved reliability and efficiency in dynamic network conditions. In scenarios with payment failure rates greater than 5%, the proposed approach achieves a 10% higher payment success rate than existing methods, while maintaining balanced performance on economic key metrics such as the payment fee and throughput, and on sustainability key metrics such as payment path length, number of inter-country/continental hops, and average carbon intensity.}
}
@COMMENT{Bibtex file generated on }