Reinforcement Learning Representations

This chapter examines the application of reinforcement learning methodologies, highlighting the significance of geometric representations in optimal policy formation. It discusses the interplay between representation learning and generalization, emphasizing the importance of creating expressive representations for adapting to varying reward functions and environments. Additionally, the speakers delve into the distributional approach to reinforcement learning, contrasting it with traditional techniques and exploring its benefits and conditions for effective implementation.