Industrial Benchmark
Java Python
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README.markdown

Industrial Benchmark

Requires: Java 8 and Apache Maven 3.x or Python 2.7

Documentation: The documentation is available online at: https://arxiv.org/abs/1709.09480

Source: D. Hein, S. Depeweg, M. Tokic, S. Udluft, A. Hentschel, T.A. Runkler, and V. Sterzing. "A benchmark 
	environment motivated by industrial control problems," in 2017 IEEE Symposium Series on Computational 
	Intelligence (SSCI), 2017, pp. 1-8. 

Citing Industrial Benchmark

To cite Industrial Benchmark, please reference:

D. Hein, S. Depeweg, M. Tokic, S. Udluft, A. Hentschel, T.A. Runkler, and V. Sterzing. "A benchmark environment 
	motivated by industrial control problems," in 2017 IEEE Symposium Series on Computational Intelligence 
	(SSCI), 2017, pp. 1-8. 

Additional references:

D. Hein, S. Udluft, M. Tokic, A. Hentschel, T.A. Runkler, and V. Sterzing. "Batch reinforcement 
	learning on the industrial benchmark: First experiences," in 2017 International Joint Conference on Neural
	Networks (IJCNN), 2017, pp. 4214–4221.

S. Depeweg, J. M. Hernández-Lobato, F. Doshi-Velez, and S. Udluft. "Uncertainty decomposition 
	in Bayesian neural networks with latent variables." arXiv preprint arXiv:1605.07127, 2017.

S. Depeweg, J. M. Hernández-Lobato, F. Doshi-Velez, and S. Udluft. "Learning and
	policy search in stochastic dynamical systems with Bayesian neural networks." arXiv
	preprint arXiv:1605.07127, 2016.
	
D. Hein, A. Hentschel, T. A. Runkler, and S. Udluft, "Particle Swarm Optimization for Model Predictive 
	Control in Reinforcement Learning Environments," in Y. Shi (Ed.), Critical Developments and Applications 
	of Swarm Intelligence, 2018, IGI Global, Hershey, PA, USA, pp. 401–427.