MPFlow: Learning Budgeted Max-Flow Optimization on the Lightning Network with Deep Graph Reinforcement Learning
arXiv:2607.08703v1 Announce Type: new Abstract: We address liquidity placement in the Bitcoin Lightning Network (LN): given a fixed budget, which channels should a node open to maximize its routing capacity? We cast this as a budget-constrained combinatorial optimization problem on graphs, selecting $k$ edge additions that maximize $s$--$t$ max-flow, a theory-grounded measure of routing capacity, and solve it with graph reinforcement learning. Our lightweight agent combines a message-passing policy network with proximal policy optimization (PPO) and action masking, and is trained under a hub-e
The increasing adoption and complexity of the Bitcoin Lightning Network necessitate advanced optimization techniques for efficient operation and scalability.
This research provides a method for more efficiently allocating liquidity on the Lightning Network, potentially improving its routing capacity and overall utility for cryptocurrency transactions.
A new, AI-driven approach for optimizing network liquidity could make the Lightning Network more robust and accessible, affecting its practical application in digital payments.
- · Bitcoin Lightning Network users
- · Cryptocurrency payment processors
- · Reinforcement learning researchers
- · Decentralized finance sector
- · Inefficient liquidity providers
- · Centralized payment systems (long-term)
More efficient routing and lower transaction costs on the Lightning Network.
Increased adoption of the Lightning Network for micro-payments and cross-border transactions.
Potential for broader integration of cryptocurrencies into mainstream financial systems due to improved infrastructure.
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Read at arXiv cs.LG