TY - JOUR

T1 - Hybrid pathfinding optimization for the Lightning Network with Reinforcement Learning

AU - Valko, Danila

AU - Kudenko, Daniel

N1 - Publisher Copyright: © 2025 Elsevier Ltd

PY - 2025/4/15

Y1 - 2025/4/15

N2 - 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.

AB - 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.

KW - Dynamic optimization with Reinforcement Learning

KW - Pathfinding algorithms

KW - Payment channel networks

KW - Payment success rate in Lightning Network

UR - http://www.scopus.com/inward/record.url?scp=85217647108&partnerID=8YFLogxK

U2 - 10.1016/j.engappai.2025.110225

DO - 10.1016/j.engappai.2025.110225

M3 - Article

AN - SCOPUS:85217647108

VL - 146

JO - Engineering Applications of Artificial Intelligence

JF - Engineering Applications of Artificial Intelligence

SN - 0952-1976

M1 - 110225

ER -