Hierarchical Relative Entropy Policy Search: An Information Theoretic Learning Algorithm in Multimodal Solution Spaces for Real Robots
by Gerhard Neumann, Christian Daniel
This specialized work explores hierarchical relative entropy policy search, an information-theoretic learning algorithm for multimodal solution spaces. With a focus on real robots, it offers a technical perspective on connecting machine learning methods, policy search, and robotic applications.
About This Book
This work examines hierarchical relative entropy policy search as an information-theoretic approach to learning.
It focuses on multimodal solution spaces, where multiple possible solutions may need to be represented and explored.
The book addresses applications in real-robot settings and connects algorithmic learning with robotic control.
Written by Gerhard Neumann and Christian Daniel, it presents a specialized contribution to robotics, artificial intelligence, and machine learning.
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