Reinforcement learning has moved from the margins of artificial intelligence to the center of the field. Andrew Barto and Rich Sutton, two researchers who helped make the approach practical, have been awarded the Turing Award, the highest honor in computer science.
The recognition matters because the method they championed is now tied to some of the most visible advances in AI. It has influenced systems such as ChatGPT, helped produce expert-level game play, and continues to shape work on AI agents, robotics, and large language models.
A bet on learning from experience
In the 1980s, Barto and Sutton were pursuing an idea that many in the field viewed as unlikely to succeed. The premise was simple in outline but difficult in practice: computers could learn by trying actions, receiving positive or negative feedback, and improving over time.
Barto is a professor emeritus at the University of Massachusetts Amherst. Sutton is a professor at the University of Alberta. Together, they helped develop reinforcement learning into a core machine learning technique, even during a period when other approaches to AI attracted more support.
At its heart, reinforcement learning is about behavior. A system takes an action, gets feedback, and adjusts what it does next. That makes it different from approaches that depend mainly on fixed logical rules or examples labeled in advance by people.
Barto described the early period plainly: “When this work started for me, it was extremely unfashionable,” he said. Looking back at the field’s rise, he added, “It’s been remarkable that [it has] achieved some influence and some attention,” he adds.
From Go to data centers and chip design
One of the clearest public demonstrations of reinforcement learning came in 2016, when Google DeepMind used it to build AlphaGo. The program learned how to play Go, a complex and subtle board game, at an expert level.
That success helped renew interest in the technique. Since then, reinforcement learning has been applied across a wide range of domains named in the source article, including advertising, optimizing data-center energy use, finance, and chip design.
Robotics is another important setting for the approach. Physical machines often need to learn through trial and error because real-world tasks can be hard to reduce to a clean set of instructions. Reinforcement learning gives researchers a way to train machines through feedback as they attempt those tasks.
The method is also now important in large language models. According to the source, reinforcement learning has helped guide the output of LLMs and support the development of highly capable chatbot programs. It is also being used to train AI models to mimic human reasoning and to build more capable AI agents.
Why the method fits modern AI
Modern AI systems are increasingly expected to do more than classify information or generate text. They are being pushed toward planning, acting, adapting, and responding to feedback. That makes reinforcement learning especially relevant.
The technique gives developers a way to connect goals with behavior. Instead of only asking whether a model can predict the next item in a sequence, reinforcement learning asks whether the system’s actions move it toward a desired outcome.
Sutton drew a distinction that remains central to current AI development. In the systems used to guide LLMs, humans provide goals rather than leaving an algorithm to learn purely through its own exploration. He argued that fully experience-based machine learning may ultimately have greater potential.
As Sutton put it, “The big division is whether [AI is] learning from people or whether it’s learning from its own experience,” he says.
That distinction helps explain why the Turing Award is not only a historical honor. It also points to an active question in AI: how much future progress will come from human-guided systems, and how much from systems that discover effective behavior through their own interaction with the world.
A long path through computer science history
Reinforcement learning has roots that reach back to the beginning of AI. Alan Turing suggested in his famous 1950 paper “Computing Machinery and Intelligence” that machines might learn through experience and feedback while exploring whether a machine could someday think like a human.
Arthur Samuel, another AI pioneer, used reinforcement learning in 1955 to create one of the first machine learning programs: a system that could play checkers.
Yet the field did not move in a straight line. Reinforcement learning and related research on artificial neural networks lost favor for years. During that period, symbolic AI and rule-based approaches received more attention than efforts to build intelligence from learning.
Barto, Sutton, and other researchers kept working on the problem. They drew on biology, psychology, neuroscience, and control theory to develop algorithms that could make this form of learning work in computers.
The Association for Computing Machinery, which presents the Turing Award annually, highlighted specific contributions that helped make the field practical. These included policy-gradient methods, which help an algorithm learn how to behave, and temporal difference learning, which lets a model keep learning over time.
Progress, risk, and cautious optimism
The same qualities that make reinforcement learning powerful also create difficult safety questions. A system trained through rewards can learn behavior that technically follows a signal while producing outcomes people do not want.
Barto said those concerns were visible from the early days. The source describes examples such as a robot repeatedly crashing because it focused on the wrong stimuli. That kind of failure shows why feedback design matters so much.
Ethical debates around AI have therefore included reinforcement learning for a long time. Barto said several of his former students are now professors studying such risks.
At the same time, he argued that the potential benefits remain large. The source connects reinforcement learning and AI to possible scientific solutions for climate change and other major problems. Barto’s conclusion was careful rather than sweeping: “If used with caution, it can be extremely helpful,” he says.
The Turing Award for Barto and Sutton recognizes more than past persistence. It marks the arrival of reinforcement learning as a central pillar of modern artificial intelligence, with influence that now reaches from board games and robots to chatbots, AI agents, and future systems designed to learn from experience.