Welcome to NEORL Website!

Latest News:

• June 5, 2022: Stable release 1.8 is out.

• Major features of 1.8 release: ensemble optimizers are added (AEO, EDEV, EPSO, HCLPSO).

• November 24, 2021: Stable release 1.7 is out.

• September 10, 2021: First NEORL stable release 1.6 is out.

Primary contact to report bugs/issues: Majdi I. Radaideh (radaideh@mit.edu)

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NEORL (NeuroEvolution Optimisation with Reinforcement Learning) is a set of implementations of hybrid algorithms combining neural networks and evolutionary computation based on a wide range of machine learning and evolutionary intelligence architectures. NEORL aims to solve large-scale optimization problems relevant to operation & optimisation research, engineering, business, and other disciplines.

Github repository: https://github.com/mradaideh/neorl

NEORL paper: https://arxiv.org/abs/2112.07057

Copyright

This repository and its content are copyright of Exelon Corporation © in collaboration with MIT Nuclear Science and Engineering 2021. All rights reserved.

You can read the first successful application of NEORL for nuclear fuel optimisation in this News Article.

User Guide

• General Guide
  • 1. Quick Installation
  • 2. Detailed Installation
  • 3. Getting Started
  • 4. Reinforcement Learning
  • 5. Evolutionary Algorithms
  • 6. Hyperparameter Tuning
• Algorithms
  • Neural Algorithms (Reinforcement Learning)
  • Evolutionary and Swarm Algorithms
  • Hybrid and Neuroevolution Algorithms
• Hyperparameter Tuning
  • 1. Grid Search
  • 2. Random Search
  • 3. Bayesian Search
  • 4. Evolutionary Search
• Examples
  • 1. Example 1: Traveling Salesman Problem
  • 2. Example 2: Ackley with EA
  • 3. Example 3: Welded-beam design
  • 4. Example 4: Benchmarks
  • 5. Example 5: CEC’2017 Test Suite
  • 6. Example 6: Three-bar Truss Design
  • 7. Example 7: Pressure Vessel Design
  • 8. Example 8: Pressure Vessel Design with Demonstration of Categorical Parameter
  • 9. Example 9: Cantilever Stepped Beam
  • 10. Example 10: Knapsack Problem
  • 11. Example 11: Microreactor Control with Malfunction

• Change Log
• Projects
• Contributors
• Contribution Guide

Projects/Papers Using NEORL

1- Radaideh, M. I., Wolverton, I., Joseph, J., Tusar, J. J., Otgonbaatar, U., Roy, N., Forget, B., Shirvan, K. (2021). Physics-informed reinforcement learning optimization of nuclear assembly design. Nuclear Engineering and Design, 372, p. 110966 [LINK1].

2- Radaideh, M. I., Shirvan, K. (2021). Rule-based reinforcement learning methodology to inform evolutionary algorithms for constrained optimization of engineering applications. Knowledge-Based Systems, 217, p. 106836 [LINK2].

3- Radaideh, M. I., Forget, B., Shirvan, K. (2021). Large-scale design optimisation of boiling water reactor bundles with neuroevolution. Annals of Nuclear Energy, 160, 108355 [LINK3].

Citing the Project

To cite this repository in publications:

@article{radaideh2021neorl,
  title={NEORL: NeuroEvolution Optimization with Reinforcement Learning},
  author={Radaideh, Majdi I and Du, Katelin and Seurin, Paul and Seyler, Devin and Gu, Xubo and Wang, Haijia and Shirvan, Koroush},
  journal={arXiv preprint arXiv:2112.07057},
  year={2021}
}

Acknowledgments

NEORL was established in MIT back to 2020 with feedback, validation, and usage of different colleagues: Issac Wolverton (MIT Quest for Intelligence), Joshua Joseph (MIT Quest for Intelligence), Benoit Forget (MIT Nuclear Science and Engineering), Ugi Otgonbaatar (Exelon Corporation).