AI pioneers Andrew Barto and Richard Sutton win 2025 Turing Award for groundbreaking contributions to reinforcement learning

The computing world is celebrating a major milestone as Andrew Barto, professor emeritus at the University of Massachusetts Amherst, and Richard Sutton, professor of computer science at the University of Alberta, Canada, have been awarded the 2024 Association for Computing Machinery A.M. Turing Award — often called the "Nobel Prize of computing" — for "developing the conceptual and algorithmic foundations of reinforcement learning."

The legacy in reinforcement learning

Barto and Sutton are widely recognized as pioneers of the modern computational reinforcement learning (RL), a field that addresses the challenge of learning how to act based on evaluative feedback. Their work has laid the conceptual and algorithmic foundations of RL, shaping the future of artificial intelligence and decision-making systems.

The influence of RL extends across multiple disciplines, including computer science (machine learning), engineering (optimal control), mathematics (operations research), neuroscience (optimal decision-making), psychology (classical and operant conditioning) and economics (rational choice theory). Researchers in these fields continue to be profoundly shaped by the contributions of Sutton and Barto.

From NSF Grants to AI Breakthroughs

Barto's contributions were made possible through a series of U.S. National Science Foundation-funded projects that sustained AI research long before its recent boom. His research was supported through grants from NSF programs including the National Robotics Initiative, Robust Intelligence, Collaborative Research in Computation Neuroscience, Human-Centered Computing, Biological Information Technology and Systems, Artificial Intelligence and Cognitive Science, which have driven the long-term, fundamental advances in machine learning that we see today.

"Barto's research exemplifies the power of foundational computational research that has not only advanced state-of-the-art decision-making machines and intelligent systems but has also provided critical insights into understanding intelligence itself," said Greg Hager, NSF assistant director for Computer and Information Science and Engineering.

"Andy Barto's work laid the foundation for modern reinforcement learning, influencing generations of researchers, including myself. His insights with Rich Sutton into how agents can learn and adapt in complex environments form the backbone of how automated behavior is generated in the field of artificial intelligence. Without his pioneering research, many of today's — and tomorrow's — AI breakthroughs wouldn't be possible," said Michael Littman, director for the NSF Division of Information and Intelligent Systems.

The impact of Barto and Sutton's work

For decades, NSF has supported fundamental research in AI, with Barto's work being among the most influential. Barto and Sutton formalized RL concepts through decades of research, beginning with Sutton's time as Barto's first doctoral student. Their collaboration continued as Sutton later joined Barto at the UMass Amherst as a senior research scientist from 1995 to 1998 and beyond, producing many of the foundational RL approaches that remain in use today.

Reinforcement learning methods built on Sutton and Barto's work today underpin:

• Chatbots: Conversational AI agents learn to answer questions helpfully and accurately with the help of a technique called reinforcement learning from human feedback, as deployed in ChatGPT and other leading bots.
• Games: From Jeopardy to Go to video games, RL algorithms have made it possible for computer players to achieve world-class performance and have even influenced the strategies of the best human players.
• Robot motor skill learning: RL enables robots to learn autonomously through trial and error how to carry out intricate tasks.
• Microprocessor layout and circuit design: RL systems make decisions for composing components that make up computer chips
• Personalized recommendations: Online services like Netflix and YouTube rely on RL techniques to tailor recommendations.
• Autonomous vehicles: RL models help self-driving cars learn how to navigate complex traffic environments.
• Supply chain optimization: RL-enabled systems learn what items need to be stored where so that customers can receive goods quickly and cheaply.
• Algorithm design: Researchers have broken new ground and solved long-standing problems with the help of RL systems.

Breakthroughs in RL have fueled a multibillion-dollar industry, with major companies like DeepMind and OpenAI relying on RL as a core technology. Additionally, many major tech firms now have dedicated RL research teams. It is also recognized as a core topic of study. For example, RL was added to the Computer Science Standards of Learning for Virginia Public Schools earlier this year.

Bridging AI and neuroscience

The influence of Barto and Sutton's work extends far beyond computer science and AI, forging crucial connections between RL and brain sciences, including cognitive science, psychology and neuroscience. Their research has provided groundbreaking insights into how learning can occur, both in machines and in the human brain.

One of their earliest breakthroughs came in 1981 when they showed that temporal difference (TD) learning could explain certain learning behaviors that the existing Rescorla-Wagner model couldn't. This discovery opened the door to a new way of understanding how learning happens. Building on this idea, a 1995 study found a connection between the TD algorithm and how dopamine neurons in the brain behave. This insight laid the groundwork for later experiments that confirmed that TD learning accurately describes how dopamine influences reward-based learning.

With the 2025 A.M. Turing Award recognizing Barto and Sutton's lifetime achievements, their legacy underscores the importance of sustained federal investment in basic research — the kind of support that has fueled AI's breakthroughs over the last four decades.

For more details on this year's award, please visit https://amturing.acm.org/