Bootstrapping of Parameterized Skills Through Hybrid Optimization in Task and Policy Spaces

Bootstrapping of Parameterized Skills Through Hybrid Optimization in Task and Policy Spaces

Modern robotic applications create high demands on adaptation of actions with respect to variance in a given task. Reinforcement learning is able to optimize for these changing conditions, but relearning from scratch is hardly feasible due to the high number of required rollouts. We propose a parame...

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Bibliographic Details
Main Authors: Jeffrey F. Queißer, Jochen J. Steil
Format: Article
Language:English
Published: Frontiers Media S.A. 2018-06-01
Series:Frontiers in Robotics and AI
Subjects:
reinforcement learning
policy optimization
memory
learning
hybrid optimization
dimensionality reduction
Online Access:https://www.frontiersin.org/article/10.3389/frobt.2018.00049/full

Internet

https://www.frontiersin.org/article/10.3389/frobt.2018.00049/full

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