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.
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.