TalkRL: The Reinforcement Learning Podcast cover image

Amy Zhang

TalkRL: The Reinforcement Learning Podcast

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Understanding Block MDPs and Abstractions

This chapter explores block Markov Decision Processes (block MDPs) and their advantages in structured environments, focusing on latent state spaces and model irrelevance abstraction. It also examines state representations, bisimulation, and the significance of coarser abstractions in reinforcement learning, illustrated through practical examples.

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