"In #distributional #Reinforcement_Learning, the #AI algorithm predicts a full spectrum of future rewards: some are more optimistic and amplify their reward signals when the reward is larger than expected; others more pessimistic, lowering their reward signals when it's smaller than predicted." "Partnering with Harvard, the teams tested out their idea in the brains of mice. In contrast to neuroscience canon, the team said, reward neurons didn't act as one. Rather than collectively encoding for a single expected outcome, they were each "tuned" to a different prediction, with some expecting a larger amount of reward, and others less hopeful, predicting smaller volumes" "We found that reward neurons in the brain were each tuned to different levels of pessimism or optimism. If they were a choir, they wouldn't all be singing the same note, but harmonizing" "In other words, they seemed to operate on very similar principles to distributed reinforcement learning, a powerful method in #AI." https://lnkd.in/grTTXeA