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What is Temporal Abstraction in Reinforcement Learning about?

This document discusses temporal abstraction in reinforcement learning using options. Options generalize actions by allowing temporally extended courses of action. An option is defined by its initiation set, policy, and termination condition. Using options defines a semi-Markov decision process that can be analyzed using methods like value iteration and Q-learning that have been generalized for SMDPs. The document outlines how options can provide temporal abstraction and how their internal structure and pol

Author

alan

Language

EN