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I’m most excited about what is now being called scientific machine learning, ie machine learning models explicitly structured to learn interpretable models that can be used to understand some scientific domain better. For example, I’m starting to work on using graph RNNs to study dynamical behavior of reinforcement learning agents and how it might give us insights into psychiatric disorders.

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I'm interested in reading more about this area of work. Can you share your project page if it exists or any foundational papers in this space

My inspiration comes from this: https://arxiv.org/pdf/1809.06303.pdf

There's a long history of using differential equation models to model high-level brain function (see also neural mass models and neural field models) but I think there are advantages to using discrete time approximations such as neural networks in an RL setting to investigate how the dynamics (e.g. attractor states, etc) map onto behavior.

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