From Semantics to Execution: Integrating Action Planning With Reinforcement Learning for Robotic Causal Problem-Solving

From Semantics to Execution: Integrating Action Planning With Reinforcement Learning for Robotic Causal Problem-Solving

Reinforcement learning is generally accepted to be an appropriate and successful method to learn robot control. Symbolic action planning is useful to resolve causal dependencies and to break a causally complex problem down into a sequence of simpler high-level actions. A problem with the integration...

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Bibliographic Details
Main Authors: Manfred Eppe, Phuong D. H. Nguyen, Stefan Wermter
Format: Article
Language:English
Published: Frontiers Media S.A. 2019-11-01
Series:Frontiers in Robotics and AI
Subjects:
reinforcement learning
hierarchical architecture
planning
robotics
neural networks
causal puzzles
Online Access:https://www.frontiersin.org/article/10.3389/frobt.2019.00123/full

Internet

https://www.frontiersin.org/article/10.3389/frobt.2019.00123/full

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