Richard S. Sutton facts for kids

Quick facts for kids Richard S. Sutton FRS FRSC
Nationality	Canadian
Citizenship	Canadian
Alma mater	University of Massachusetts Amherst Stanford University
Known for	Temporal difference learning, Dyna, Options, GQ(λ)
Awards	AAAI Fellow (2001) President's Award (INNS) (2003) Royal Society of Canada Fellow (2016) Turing Award (2025)
Scientific career
Fields	Artificial Intelligence Reinforcement Learning
Institutions	University of Alberta
Thesis	Temporal credit assignment in reinforcement learning (1984)
Doctoral advisor	Andrew Barto
Doctoral students	David Silver, Doina Precup

Richard S. Sutton is a Canadian computer scientist. He is a professor at the University of Alberta. He also works as a research scientist at Keen Technologies.

Many people see him as one of the main people who helped create modern reinforcement learning. This is a special part of AI. He made important discoveries like temporal difference learning and policy gradient methods.

Early Life and Education

Richard Sutton was born in Ohio, USA. He grew up in Oak Brook, Illinois, which is a town near Chicago.

He earned his first degree in psychology from Stanford University in 1978. Later, he studied computer science at the University of Massachusetts Amherst. He received his master's degree in 1980 and his Ph.D. in 1984. His main teacher there was Andrew Barto.

Richard Sutton's Ph.D. paper was about Temporal Credit Assignment in Reinforcement Learning. In this paper, he introduced new ideas for how computers can learn. He was inspired by Harry Klopf's work from the 1970s. Klopf believed that computers needed to learn by trial and error, not just by being told what to do. This idea made Sutton very interested in reinforcement learning.

His Career Journey

After finishing his Ph.D. in 1984, Sutton worked as a researcher at the University of Massachusetts. From 1985 to 1994, he worked at a company called GTE in Massachusetts. He was a main technical staff member there.

Then, he spent three years back at the University of Massachusetts Amherst. He worked as a senior research scientist. From 1998 to 2002, Sutton worked at the AT&T Shannon Laboratory. He was a main technical staff member in their artificial intelligence department.

Since 2003, he has been a professor at the University of Alberta in Canada. He led the university's Reinforcement Learning and Artificial Intelligence Laboratory until 2018. In 2017, he also joined Deepmind, a big AI company. He helped start their office in Edmonton, Canada.

Richard Sutton became a Canadian citizen in 2015. He gave up his US citizenship in 2017.

What is Reinforcement Learning?

In the early 1980s, Richard Sutton joined Andrew Barto at UMass. They wanted to understand how brain cells (neurons) help humans think. They used math to turn these ideas into a way for computers to learn. This new way of learning became known as reinforcement learning. It is now a very important part of artificial intelligence.

Barto and Sutton used something called Markov decision processes (MDPs). This is a math tool to explain how computer programs, called agents, make choices. These agents are in a changing environment. They get rewards for their actions.

Before Sutton and Barto, MDPs usually assumed the agent knew everything about its environment. But Sutton and Barto's reinforcement learning allowed the agent to learn even when it didn't know everything. This made reinforcement learning useful for many more real-world problems.

Richard Sutton returned to Canada in the 2000s. He kept working on reinforcement learning. This field grew a lot, and one of its first big successes was Google's AlphaGo program. AlphaGo used reinforcement learning to beat the world champion in the game Go. Barto and Sutton are seen as the pioneers of modern reinforcement learning. Their work is key to today's AI boom.

In 2019, Sutton wrote an essay about AI research. He said that AI researchers often try to build human-like thinking into computers. But he argued that general methods that use a lot of computing power work best in the long run. He believed these methods beat efforts that rely on human knowledge about specific areas.

In 2023, Sutton and John Carmack announced they would work together. They plan to develop AGI. AGI is a type of AI that can understand or learn any intellectual task that a human being can.

Awards and Honors

Richard Sutton has received many important awards for his work.

• He became a fellow of the Association for the Advancement of Artificial Intelligence (AAAI) in 2001. This was for his important work in machine learning and reinforcement learning.
• In 2003, he received the President's Award from the International Neural Network Society.
• The University of Massachusetts Amherst gave him the Outstanding Achievement in Research award in 2013.
• In 2016, Sutton was chosen as a Fellow of the Royal Society of Canada.
• He was elected a Fellow of the Royal Society of London in 2021.
• In 2025, he received the Turing Award with Andrew Barto. This award is like the Nobel Prize for computing. They won it for creating the main ideas and methods behind reinforcement learning.