>"Hard" problems are multi-optimization, where even the definitions aren't very clear: "learn as much as possible and live a happy life while making a productiv

>"Hard" problems are multi-optimization, where even the definitions aren't very clear: "learn as much as possible and live a happy life while making a productive living for yourself". Turn that into an objective function... Minimize the informational free-energy of the product of the multiplicatively inverted brain-to-body reinforcement-learned energy function of reward with the brain-to-body reinforcement-learned energy function of punishment, via active inference? Beh, those words are messy. The point is to talk about a Gibbs distribution with energy functions Reward(X, Y) and Punishment(X, Y), such that the "total" energy function is E(X, Y) \propto Punishment(X, Y) - Reward(X, Y). This then gives us a "goal distribution" for an active inference agent (like a human), defined "up to" the reinforcement-learned energy functions, whose limiting functions (need a lot of functional analysis and probabilistic reasoning over function spaces, there) are the "ground truth" causal relations by which the world causes reward signals through the body and its senses. https://arxiv.org/pdf/1503.04187.pdf https://arxiv.org/pdf/1503.04187.pdf