Deep Reinforcement Learning-based Rebalancing Policies for Profit Maximization of Relay Nodes in Payment Channel Networks

TL;DR

This paper proposes a deep reinforcement learning approach to optimize rebalancing policies for relay nodes in payment channel networks, aiming to maximize profits through submarine swaps while managing liquidity efficiently.

Contribution

It introduces the first DRL-based method for liquidity management in payment channel networks, modeling rebalancing as a Markov Decision Process and demonstrating superior performance through simulation.

Findings

DRL policy outperforms traditional rebalancing strategies in simulations.

The model effectively captures the stochastic dynamics of transaction arrivals.

Rebalancing with DRL increases relay node profits significantly.

Abstract

Payment channel networks (PCNs) are a layer-2 blockchain scalability solution, with its main entity, the payment channel, enabling transactions between pairs of nodes "off-chain," thus reducing the burden on the layer-1 network. Nodes with multiple channels can serve as relays for multihop payments by providing their liquidity and withholding part of the payment amount as a fee. Relay nodes might after a while end up with one or more unbalanced channels, and thus need to trigger a rebalancing operation. In this paper, we study how a relay node can maximize its profits from fees by using the rebalancing method of submarine swaps. We introduce a stochastic model to capture the dynamics of a relay node observing random transaction arrivals and performing occasional rebalancing operations, and express the system evolution as a Markov Decision Process. We formulate the problem of the maximization of the node's fortune over time over all rebalancing policies, and approximate the optimal solution by designing a Deep Reinforcement Learning (DRL)-based rebalancing policy. We build a discrete event simulator of the system and use it to demonstrate the DRL policy's superior performance under most conditions by conducting a comparative study of different policies and parameterizations. Our work is the first to introduce DRL for liquidity management in the complex world of PCNs.