Prefrontal cortex as a meta-reinforcement learning system DeepMind

In our new paper in Nature Neuroscience, we use the meta-reinforcement learning framework developed in AI research to investigate the role of dopamine in the brain in helping us to learn. Dopamine--commonly known as the brain's pleasure signal--has often been thought of as analogous to the reward prediction error signal used in AI reinforcement learning algorithms. These systems learn to act by trial and error guided by the reward. We propose that dopamine's role goes beyond just using reward to learn the value of past actions and that it plays an integral role, specifically within the prefrontal cortex area, in allowing us to learn efficiently, rapidly and flexibly on new tasks. We tested our theory by virtually recreating six meta-learning experiments from the field of neuroscience--each requiring an agent to perform tasks that use the same underlying principles (or set of skills) but that vary in some dimension.