Adaptive reinforcement learning-based control using proximal policy optimization and slime mould algorithm with experimental tower crane system validation

Author:

Zamfirache Iuliu Alexandru,Precup Radu-EmilORCID,Petriu Emil M.

Funder

Unitatea Executiva pentru Finantarea Invatamantului Superior a Cercetarii Dezvoltarii si Inovarii

Natural Sciences and Engineering Research Council of Canada

Publisher

Elsevier BV

Reference36 articles.

1. Neural Networks: Tricks of the Trade. Lecture Notes in Computer Science;Montavon,2012

2. J. Schulman, S. Levine, P. Abbeel, M. Jordan, P. Moritz, Trust region policy optimization. In: Proc. 32nd International Conference on Machine Learning, Lille, France, 2015, pp. 1889–1897.

3. Proximal policy optimization algorithms;Schulman;arXiv,1707

4. Adaptive stepsizes for recursive estimation with applications in approximate dynamic programming;George;Mach. Learn.,2006

5. L.N. Smith, Cyclical learning rates for training neural networks. In: Proc. 2017 IEEE Winter Conference on Applications of Computer Vision, Santa Rosa, CA, USA, 2017, pp. 464–472.

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