Requires: Java 8 and Apache Maven 3.x or Python 3.7
For the Python Version, the industrial benchmark environment is contained in industrial_benchmark_python/IDS.py, and there is an OpenAI Gym compliant wrapper in industrial_benchmark_python/IBGym.py
You can install the Benchmark as a package after cloning, using:
pip install dist/industrial_benchmark_python-2.0-py3-none-any.whl
Or directly from PyPI:
pip install industrial_benchmark_python
To test whether it works and to check out how current RL methods implemented in the stable_baselines package do on the benchmark:
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 using Industrial Benchmark:
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, 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. 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, IGI Global, Hershey, PA, USA, 2018, pp. 401–427. S. Depeweg, J. M. Hernandez-Lobato, F. Doshi-Velez, and S. Udluft. "Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning." 35th International Conference on Machine Learning, ICML 2018. Vol. 3. 2018. D. Hein, S. Udluft, and T.A. Runkler. "Interpretable policies for reinforcement learning by genetic programming." Engineering Applications of Artificial Intelligence, 76, 2018, pp. 158-169. D. Hein, S. Udluft, and T.A. Runkler. "Generating interpretable fuzzy controllers using particle swarm optimization and genetic programming," in Proceedings of the Genetic and Evolutionary Computation Conference Companion, ACM, 2018, pp. 1268-1275. N. Di Palo, and H. Valpola. "Improving Model-Based Control and Active Exploration with Reconstruction Uncertainty Optimization." arXiv preprint arXiv:1812.03955, 2018. F. Linker. "Industrial Benchmark for Fuzzy Particle Swarm Reinforcement Learning." http://felixlinker.de/doc/ib_fpsrl.pdf, 2019
Additional references mentioning Industrial Benchmark:
Y. Li. "Deep reinforcement learning: An overview." arXiv preprint arXiv:1701.07274, 2017. D. Ha, and J. Schmidhuber. "Recurrent world models facilitate policy evolution," in Advances in Neural Information Processing Systems, 2018, pp. 2450-2462. M. Schaarschmidt, A. Kuhnle, B. Ellis, K. Fricke, F. Gessert, and E. Yoneki. "Lift: Reinforcement learning in computer systems by learning from demonstrations." arXiv preprint arXiv:1808.07903, 2018. M. Kaiser, C. Otte, T.A. Runkler, and C.H. Ek. "Data Association with Gaussian Processes." arXiv preprint arXiv:1810.07158, 2018. D. Lee, and J. McNair. "Deep reinforcement learning agent for playing 2D shooting games." Int. J. Control Autom, 11, 2018, pp. 193-200. D. Marino, and M. Manic. "Modeling and planning under uncertainty using deep neural networks." IEEE Transactions on Industrial Informatics, 2019. J. Fu, A. Kumar, O. Nachum, G. Tucker, and S. Levine. "Datasets for Data-Driven Reinforcement Learning." arXiv preprint arXiv:2004.07219, 2020. M. Schaarschmidt. "End-to-end deep reinforcement learning in computer systems." PhD Thesis, University of Cambridge, 2020.