This episode of In Context features Rich Sutton, a pioneer in the field of reinforcement learning, a computational approach to learning where an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. A professor in the department of Computing Science at the University of Alberta and advisor for RBC and DeepMind, Professor Sutton has devoted much of his life to AI research. His 1998 textbook, Reinforcement Learning: An Introduction, is widely used today. 

Throughout this conversation, Rich shares his insights on the history of reinforcement learning, current developments in temporal difference learning (a subfield of reinforcement learning), the fundamental difference between reinforcement learning and supervised learning, the importance of intuition and critical thinking in scientific research, and the parallels between learning algorithms and the big choices we make in life.