alt Hacker NewsReal biological operant behavior isn't exactly trial and error learning.
Many factors shape and guide initial responses.
What I've noticed in some descriptions of models is the use of optimization for reinforcement to shape responses. In real organisms behavior may be controlled by short or long term outcomes, and may oscillate between this "optimization" based on schedules. This produces variability in the trials which can adjust behavior. Are we seeing these reinforcement models do this?
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ainch • today at 10:32 AM
There is a field of hierarchical RL in which the optimisation occurs over a range of time scales/abstraction. But I'm not aware of much practical success for these approaches so far.
I found this comment/question deeply intriguing.
I’m no expert at this and was wondering what you meant by the following:
> In real organisms behavior may be controlled by short or long term outcomes, and may oscillate between this "optimization" based on schedules
Could you perhaps provide an example that would help me understand what you mean?
Thanks for the insightful comment either way.