# Joint Combinatorial Node Selection and Resource Allocations in the Lightning Network using Attention-based Reinforcement Learning

arXiv: https://arxiv.org/abs/2411.17353 · HTML: https://mlsanity.com/paper/2411.17353/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 24 of its 59 references that are in the map.
- First posted on arXiv 26 Nov 2024.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [How Attentive are Graph Attention Networks?](https://mlsanity.com/paper/2105.14491/) (2021)
- [Hierarchical Graph Representation Learning with Differentiable Pooling](https://mlsanity.com/paper/1806.08804/) (2018)
- [Self-Attention Graph Pooling](https://mlsanity.com/paper/1904.08082/) (2019)
- [A Survey of Transformers](https://mlsanity.com/paper/2106.04554/) (2021)
- [A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges](https://mlsanity.com/paper/2403.04468/) (2024)
- [Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks](https://mlsanity.com/paper/2010.05313/) (2020)
- [Little Ball of Fur: A Python Library for Graph Sampling](https://mlsanity.com/paper/2006.04311/) (2020)
- [Topological Analysis of Bitcoin's Lightning Network](https://mlsanity.com/paper/1901.04972/) (2019)
- [A Cryptoeconomic Traffic Analysis of Bitcoin's Lightning Network](https://mlsanity.com/paper/1911.09432/) (2019)
- [How to profit from payments channels](https://mlsanity.com/paper/1911.08803/) (2019)
- [Discharged Payment Channels: Quantifying the Lightning Network's Resilience to Topology-Based Attacks](https://mlsanity.com/paper/1904.10253/) (2019)
- [A Centrality Analysis of the Lightning Network](https://mlsanity.com/paper/2201.07746/) (2022)
- [DyFEn: Agent-Based Fee Setting in Payment Channel Networks](https://mlsanity.com/paper/2210.08197/) (2022)
- [The evolution of Lightning Network's Topology during its first year and the influence over its core values](https://mlsanity.com/paper/1902.07307/) (2019)
- [On the Impact of Attachment Strategies for Payment Channel Networks](https://mlsanity.com/paper/2102.09256/) (2021)
- [Rational Economic Behaviours in the Bitcoin Lightning Network](https://mlsanity.com/paper/2312.16496/) (2023)

## Built on by (most cited first, 1 of 1)

- [Predicting Channel Closures in the Lightning Network with Machine Learning](https://mlsanity.com/paper/2605.12759/) (2026)

## Abstract (arXiv)

The Lightning Network (LN) has emerged as a second-layer solution to Bitcoin's scalability challenges. The rise of Payment Channel Networks (PCNs) and their specific mechanisms incentivize individuals to join the network for profit-making opportunities. According to the latest statistics, the total value locked within the Lightning Network is approximately 500 million. Meanwhile, joining the LN with the profit-making incentives presents several obstacles, as it involves solving a complex combinatorial problem that encompasses both discrete and continuous control variables related to node selection and resource allocation, respectively. Current research inadequately captures the critical role of resource allocation and lacks realistic simulations of the LN routing mechanism. In this paper, we propose a Deep Reinforcement Learning (DRL) framework, enhanced by the power of transformers, to address the Joint Combinatorial Node Selection and Resource Allocation (JCNSRA) problem. We have improved upon an existing environment by introducing modules that enhance its routing mechanism, thereby narrowing the gap with the actual LN routing system and ensuring compatibility with the JCNSRA problem. We compare our model against several baselines and heuristics, demonstrating its superior performance across various settings. Additionally, we address concerns regarding centralization in the LN by deploying our agent within the network and monitoring the centrality measures of the evolved graph. Our findings suggest not only an absence of conflict between LN's decentralization goals and individuals' revenue-maximization incentives but also a positive association between the two.