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
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- LinkedLinked via arxiv author · 85%Harrison Rush →
“MPFlow: Learning Budgeted Max-Flow Optimization on the Lightning Network with Deep Graph Reinforcement Learning”
- LinkedLinked via arxiv author · 85%Vincent Davis →
“MPFlow: Learning Budgeted Max-Flow Optimization on the Lightning Network with Deep Graph Reinforcement Learning”
- LinkedLinked via arxiv author · 85%Simone Antonelli →
“MPFlow: Learning Budgeted Max-Flow Optimization on the Lightning Network with Deep Graph Reinforcement Learning”
- LinkedLinked via arxiv author · 85%Vikash Singh →
“MPFlow: Learning Budgeted Max-Flow Optimization on the Lightning Network with Deep Graph Reinforcement Learning”
- LinkedLinked via arxiv author · 85%Jesse Shrader →
“MPFlow: Learning Budgeted Max-Flow Optimization on the Lightning Network with Deep Graph Reinforcement Learning”
- LinkedLinked via arxiv author · 85%Emanuele Rossi →
“MPFlow: Learning Budgeted Max-Flow Optimization on the Lightning Network with Deep Graph Reinforcement Learning”
- PossiblePossibly related (embedding) · 53%TorchTrade/torchtrade →
- FuzzySimilar title/name (fuzzy) · 87%aymericdamien/TopDeepLearning →
“Fuzzy title match (0.94): “MPFlow: Learning Budgeted Max-Flow Optimization on the Light” ≈ “aymericdamien/TopDeepLearning””