# DyFEn: Agent-Based Fee Setting in Payment Channel Networks

arXiv: https://arxiv.org/abs/2210.08197 · HTML: https://mlsanity.com/paper/2210.08197/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 31 references that are in the map.
- First posted on arXiv 15 Oct 2022.

## Builds on (most cited first, 10 of 10)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Topological Analysis of Bitcoin's Lightning Network](https://mlsanity.com/paper/1901.04972/) (2019)
- [Flash: Efficient Dynamic Routing for Offchain Networks](https://mlsanity.com/paper/1902.05260/) (2019)
- [A Cryptoeconomic Traffic Analysis of Bitcoin's Lightning Network](https://mlsanity.com/paper/1911.09432/) (2019)
- [Discharged Payment Channels: Quantifying the Lightning Network's Resilience to Topology-Based Attacks](https://mlsanity.com/paper/1904.10253/) (2019)
- [A percolation model for the emergence of the Bitcoin Lightning Network](https://mlsanity.com/paper/1912.03556/) (2019)

## Built on by (most cited first, 2 of 2)

- [Joint Combinatorial Node Selection and Resource Allocations in the Lightning Network using Attention-based Reinforcement Learning](https://mlsanity.com/paper/2411.17353/) (2024)
- [Crypto x AI, AI x Crypto: A Survey](https://mlsanity.com/paper/2606.13892/) (2026)

## Abstract (arXiv)

In recent years, with the development of easy to use learning environments, implementing and reproducible benchmarking of reinforcement learning algorithms has been largely accelerated by utilizing these frameworks. In this article, we introduce the Dynamic Fee learning Environment (DyFEn), an open-source real-world financial network model. It can provide a testbed for evaluating different reinforcement learning techniques. To illustrate the promise of DyFEn, we present a challenging problem which is a simultaneous multi-channel dynamic fee setting for off-chain payment channels. This problem is well-known in the Bitcoin Lightning Network and has no effective solutions. Specifically, we report the empirical results of several commonly used deep reinforcement learning methods on this dynamic fee setting task as a baseline for further experiments. To the best of our knowledge, this work proposes the first virtual learning environment based on a simulation of blockchain and distributed ledger technologies, unlike many others which are based on physics simulations or game platforms.