Exploring Parameter Space in Reinforcement Learning

Exploring Parameter Space in Reinforcement Learning

This paper discusses parameter-based exploration methods for reinforcement learning. Parameter-based methods perturb parameters of a general function approximator directly, rather than adding noise to the resulting actions. Parameter-based exploration unifies reinforcement learning and black-box opt...

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Bibliographic Details
Main Authors: Rückstieß Thomas, Sehnke Frank, Schaul Tom, Wierstra Daan, Sun Yi, Schmidhuber Jürgen
Format: Article
Language:English
Published: De Gruyter 2010-03-01
Series:Paladyn
Subjects:
reinforcement learning
optimization
exploration
policy gradients
Online Access:https://doi.org/10.2478/s13230-010-0002-4

Internet

https://doi.org/10.2478/s13230-010-0002-4

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