用强化学习优化比特币闪电网络的通道配置，提升路由能力。
MPFlow: Learning Budgeted Max-Flow Optimization on the Lightning Network with Deep Graph Reinforcement Learning
- 用图神经网络+强化学习，自动选择最优通道开通策略。
- 在真实网络上实验，相比传统方法提升最大流达23%以上。
- 适合需要高效管理资金流动的节点运营者使用。
我们解决比特币闪电网络中的流动性配置问题：在固定预算下，应开通哪些通道以最大化路由容量？将此问题建模为图上的预算约束组合优化，目标是通过选择 $k$ 条新增边来最大化 $s$--$t$ 最大流，该指标理论严谨且反映路由能力，并采用图强化学习求解。我们的轻量级智能体结合消息传递策略网络与近端策略优化（PPO）及动作掩码机制，训练时采用枢纽排除课程：从子图中移除顶级枢纽，迫使策略学习基于容量的通道布局而非仅依赖枢纽连接。在真实闪电网络快照上进行的广泛实验表明，该方法在多个种子和未见图上均显著优于强基线启发式算法。该智能体已投入生产用于同行推荐，累计执行4640次通道开通决策，共分配267.3 BTC，涉及1600万美元资金，覆盖30个管理节点。
原文摘要 · Abstract (English)
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-exclusion curriculum: the network's top hubs are removed from training subgraphs, forcing the policy to learn capacity-aware placement rather than hub attachment. In extensive experiments on real Lightning Network snapshots, our method consistently outperforms strong heuristic baselines on the max-flow objective across multiple seeds and unseen graphs. The agent has been deployed in production for peer recommendations, executing 4640 channel-open decisions that cumulatively allocate 267.3 BTC over $16 million across 30 managed nodes.
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