Andrew Barto and Richard Sutton earned the 2024 ACM A.M. Turing Award. This prize honors their work on reinforcement learning, a key part of artificial intelligence (AI) technology.

Barto, a professor emeritus at the University of Massachusetts Amherst, teamed up with Sutton, a professor at the University of Alberta. They started in working together in the 1980s. Reinforcement learning, or RL, teaches machines to act better by using rewards – like treats for a pet.

In RL, an agent is something that sees and does things, like a robot or program. Rewards tell the agent if its actions are good or bad. Barto and Sutton built this idea using math called Markov decision processes, or MDPs. MDPs help agents decide in random settings, aiming to get the most rewards over time. Unlike regular MDPs, RL lets the agent learn without knowing everything upfront. This makes RL useful for many problems.

They created key tools for RL, like temporal difference learning. This method helps predict rewards faster and better. They also made policy-gradient methods and used neural networks to store what the agent learns. Their work mixed learning with planning, showing how agents can study their world to make smarter choices.

The Nobel Prize of Computing

Their 1998 book, Reinforcement Learning: An Introduction, taught thousands of people about RL and still guides research today. Years later, RL joined with deep learning to do amazing things.

With RL, robots learn to move objects, like solving a Rubik’s Cube. RL improves internet ads, chip designs, and even supply chains. Plus, it helps explain how human brains work with dopamine, a chemical for rewards. Barto and Sutton’s ideas keep growing, making tech and science better. The Turing Award, often called the “Nobel Prize of Computing,” gives them $1 million from Google. Their work proves old ideas can spark huge changes, exciting everyone in tech.

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