Case Studies on Learning and Control Architectures for Autonomous Systems

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Authors
  1. Cheng, D.X.P.
Corporate Authors
Defence R&D Canada - Suffield, Ralston ALTA (CAN)
Abstract
This report reviews some of the established learning and control architectures that have been applied or have potential to apply to autonomous systems, with an emphasis on their potential for military applications. In particular, techniques of reinforcement learning, neural network based learning, and genetic algorithms are reviewed with respect to the key progress made and main problems to be addressed in each of the research fields. To illustrate implementation of the learning approaches for autonomous systems, three cases are studied: Autonomous Land Vehicle in Neural Networks (ALVINN), evolutionary approaches for training ALVINN, and pattern recognition with recurrent neural networks for autonomous systems. Strengths, limitations, and potential of the learning techniques are reviewed and discussed for future development from the perspective of autonomous systems application.

Il y a un résumé en français ici.

Keywords
Autonomous Systems;Unmanned Vehicles;Reinforcement Learning;Neural Networks;Genetic Algorithms
Report Number
DRDC-SUFFIELD-TM-2013-059 — Technical Memorandum
Date of publication
01 Jan 2013
Number of Pages
70
DSTKIM No
CA044729
CANDIS No
805068
Format(s):
Electronic Document(PDF)

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