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Recent Advances in Hierarchical Reinforcement Learning by Yixun Hu is a document available to read on EtoBox.
What is Recent Advances in Hierarchical Reinforcement Learning about?
The document discusses recent advancements in hierarchical reinforcement learning (RL), focusing on how temporal abstraction can mitigate the curse of dimensionality in RL algorithms. It reviews various approaches to hierarchical RL, including the options formalism, hierarchies of abstract machines, and the MAXQ framework, all grounded in semi-Markov decision processes. The authors also address extensions to concurrent activities and multiagent coordination, highlighting ongoing challenges in the field.
- Author
- Yixun Hu
- Language
- EN