Graph RL with action masking and curriculum learning can solve real-world network optimization problems better than heuristics, and this approach is now live managing millions in Bitcoin transactions.
This paper solves the problem of optimizing liquidity placement in Bitcoin's Lightning Network using deep reinforcement learning. Given a budget, nodes must decide which payment channels to open to maximize routing capacity. The authors train a graph neural network agent with PPO to select the best k channels, using a curriculum that prevents the model from simply copying hub nodes.