The Association for Computing Machinery has awarded the prestigious Turing Award to Andrew Barto and Richard Sutton for their groundbreaking work on reinforcement learning. As reported by Cade Metz in The New York Times, the two researchers will share the $1 million prize that comes with what is often called the “Nobel Prize of computing.”
Barto and Sutton began their collaboration in the late 1970s at the University of Massachusetts, Amherst, developing mathematical models for how machines could learn from the digital equivalent of pleasure and pain. Their 1998 book, “Reinforcement Learning: An Introduction,” remains the definitive text on the subject.
Reinforcement learning has become crucial to recent AI breakthroughs, including Google’s AlphaGo, which defeated world champion Lee Sedol in 2016, and OpenAI’s ChatGPT. The technique allows AI systems to improve through trial and error, learning which actions lead to success and which to failure.
“They are the undisputed pioneers of reinforcement learning,” said Oren Etzioni, professor emeritus at the University of Washington.
More recent applications include “reinforcement learning from human feedback” (RLHF), which helped refine ChatGPT using human evaluations, and self-learning models like OpenAI’s o1 and DeepSeek’s R1 that solve problems through repeated attempts.
Both award winners believe reinforcement learning will be central to future AI development, particularly in robotics. “Learning to control a body through reinforcement learning — that is a very natural thing,” Barto noted.