Reinforcement learning is important for understanding behavior because it tells us how actions are guided by reward. But the topic also has a broader significance---as an example of the happy marriage that can come from blending computer science, psychology and neuroscience. In this way, RL is a poster child for what's known as Marr's levels analysis, an approach to understanding computation that essentially asks why, how, and where. On this episode we first define some of the basic terms of reinforcement learning (action, state, environment, policy, value). Then we break it down according to Marr's three levels: what is the goal of RL? How can we (or an artificial intelligence) learn better behavior through rewards? and where in the brain is this carried out? Also we get into the relationship between reinforcement learning and evolution, discuss what counts as a reward, and try to improvise some relatable examples involving cake, cigarettes, chess, and tomatoes.

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