I've been thinking a lot about the relationship between reinforcement learning and neuroscience lately, and something about the usual framing doesn't quite capture it.
People often say the two fields developed in parallel. But historically it feels more like a spiral.
Ideas move from neuroscience into computational models, then back again. Each turn sharpens the other.
I'm considering writing a deep dive series about this, tentatively called “The RL Spiral.” The goal would be to trace how ideas moved back and forth between the two fields over time, and how that process shaped modern reinforcement learning.
Some topics I'm thinking about:
- Thorndike, behaviorism, and the origins of reward learning
- Dopamine as a reward prediction error signal
- Temporal Difference learning and the Sutton–Barto framework
- How neuroscience experiments influenced RL algorithms (and vice versa)
- Actor–critic and basal ganglia parallels
- Cybernetics and Active Inference
- Exploration vs curiosity in animals and agents
- What modern deep RL and world models might learn from neuroscience
Curious if people here would find something like this interesting.
Also very open to suggestions.
What parts of the RL ↔ neuroscience connection would you most want a deep dive on?
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