We're interested in combining probing data with gossip data to create a reinforcement learning
My hunch is RL on a dynamic graph won't perform well. Have you defined a problem statement for training the agent?
A dynamic graph is precisely why you would use a learning agent
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The dynamic graph of LN probes and gossip data has many temporal dependencies. I.e. past actions affect the topology of the graph. It is challenging to account for these temporal patterns in the problem statement of the RL agent.
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We haven't done any work on it yet. If it were strictly based on probing data, I don't think it would work because the graph is dynamic. But there might be something in there by feeding in the dynamic parts via the historical gossip we are saving, combined with the probing data. Even the gossip alone might help identify trends and changes in liquidity / fees across the network.
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