Centralization versus Decentralization in Reinforcement Learning

The speakers debate the concept of centralization versus decentralization in reinforcement learning, discussing whether the reward signal can always be simplified to a scalar. They explore the idea of sub goals and their connection to the ultimate goal, as well as the complexity and limitations of evaluating scalar reinforcement. The chapter concludes with a discussion on the uncertainty of outcomes and the mystery of the world.