Looks like a nice paper on the inadequacy of RL to model human-like intelligence

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Eray Ozkural

Jan 19, 2017, 10:01:20 PM1/19/17
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Homeostatic reinforcement learning for integrating reward collection and physiological stability


Efficient regulation of internal homeostasis and defending it against perturbations requires adaptive behavioral strategies. However, the computational principles mediating the interaction between homeostatic and associative learning processes remain undefined. Here we use a definition of primary rewards, as outcomes fulfilling physiological needs, to build a normative theory showing how learning motivated behaviors may be modulated by internal states. Within this framework, we mathematically prove that seeking rewards is equivalent to the fundamental objective of physiological stability, defining the notion of physiological rationality of behavior. We further suggest a formal basis for temporal discounting of rewards by showing that discounting motivates animals to follow the shortest path in the space of physiological variables toward the desired setpoint. We also explain how animals learn to act predictively to preclude prospective homeostatic challenges, and several other behavioral patterns. Finally, we suggest a computational role for interaction between hypothalamus and the brain reward system.

I've always said the same, an RL agent might be like a really smart insect, but it would likely not have human-like behavior. There does seem to be more to what the default mode network does.
I have written a bit on the latest neuroscience theory in relation to this observation, I will share it with you later.


Eray Ozkural, PhD. Computer Scientist
Founder, Gok Us Sibernetik Ar&Ge Ltd.
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