Andrew G. Barto is a former computer scientist and professor emeritus and known for his research on learning in machines and animals. He worked on developing learning algorithms that are useful for engineering applications but that also make contact with learning as studied by psychologists and neuroscientists.
When it comes to reinforcement learning (RL), it has been immensely gratifying for Andrew to participate in establishing new links between RL and methods from the theory of stochastic optimal control. Especially exciting are the connections between temporal difference (TD) algorithms and the brain's dopamine system.
Plenty of people know him as one of the authors of the great book “Reinforcement Learning: An Introduction” which he wrote with his colleague Richard Sutton.
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