English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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

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