MPFlow 用强化学习帮你决定闪电网络通道开在哪，已经管了 267.3 BTC，比传统方法靠谱。
MPFlow 将比特币闪电网络的流动性放置问题建模为预算约束下的图组合优化，通过选择 k 条边添加来最大化 s-t 最大流。该方法结合消息传递策略网络和 PPO 算法，采用中心节点排除训练策略。在真实闪电网络快照上，MPFlow 在多个种子和未见图上一致优于强启发式基线。该代理已部署生产，执行 4640 次通道开放决策，在 30 个托管节点上累计分配 267.3 BTC（超 1600 万美元）。
MPFlow: Learning Budgeted Max-Flow Optimization on the Lightning Network with Deep Graph Reinforcement Learning
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.