Reinforcement learning is a framework open to such a deep level of investigation. There are theoretical proofs showing convergence, new algorithms, relations to biology, and my personal favorite: applications. RL is becoming feasible to use in real-world systems and this has potentially huge implications (see this write-up by a colleague) because it’s interactions and problem definition are not well-posed. Legislating this so safety and usefulness are preserved is an active area of my work.
I spend most of my time thinking about the variant of model-based reinforcement learning, which involves very similar optimizations, but has a structured and modular learning setup. Learning a dynamics model lends itself to interpretability and generalization (see model-learning).
