IntelliHealer

IntelliHealer: An imitation and reinforcement learning platform for
self-healing distribution networks. IntelliHealer uses imitation learning framework to learn restoration policy
for distribution system service restoration so as to perform the restoration
actions (tie-line switching and reactive power dispatch) in real time and in
embedded environment.

It is worth mentioning that the imitation lealrning framework acts as a bridge between reinforcement learning-based
techniques and mathematical programming-based methods and a way to leverage well-studied mathematical programming-based
decision-making systems for reinforcement learning-based automation.

Scope: Training restoration agent	Framework: imitation learning

Such embeddable and computation-free policies allows us to integrate the
self-healing capability into intelligent devices
A polit project conducted by the S&C Electric
can be found here.
For details of this work, please refer to our paper at
arXiv
or IEEE.

Features

• IntelliHealer proposes the imitation learning framework,
  which improve the sample efficiency using a mixed-integer program-based expert
  compared with the traditional exploration-dominant reinforcement learning algorithms.

  

• IntelliHealer proposes a hierarchical policy network,
  which can accommodate both discrete and continuous actions.

  

• IntelliHealer provides an OpenAI-Gym environment for
  distribution system restoration,
  which can be connected to Stable-Baselines3,
  a state-of-the-art collection of reinforcement learning algorithms. Currently, the Gym environment
  contains two test feeders: 33-node and 119-node system.

• IntelliHealer provides distribution system optimization models built on Pyomo,
  whicn can be used to develop other problem formulations.

Documentation

For installation instructions, basic usage and benchmarks results, see the official documentation.

Acknowledgments

Based upon work supported by the U.S. Department of Energy Advanced Grid Modeling Program under Grant DE-OE0000875.

Citing

If you find this code useful in your research, please consider citing:

Y. Zhang, F. Qiu, T. Hong, Z. Wang, and F. Li, “Hybrid imitation learning for real-time service restoration in resilient distribution systems,” IEEE Trans. Ind. Informat., pp. 1-11,early access, 2021, doi: 10.1109/TII.2021.3078110.

@article{Zhang2021_IntelliHealer,
author = {Zhang, Yichen and Qiu, Feng and Hong, Tianqi and Wang, Zhaoyu and Li, Fangxing Fran},
journal = {IEEE Trans. Ind. Informat.},
keywords = {Deep learning,Imitation learning,Mixed-integer linear programming,Reinforcement learning,Resilient distribution system,Service restoration},
pages = {1--11},
note={early access},
title = {{Hybrid imitation learning for real-time service restoration in resilient distribution systems}},
year = {2021}
}

Related Works

Regarding Imitation and Reinforcement Learning

The framework development is based on the following work:

• Ross, Stéphane, and Drew Bagnell. “Efficient reductions for imitation learning.” In Proceedings of the thirteenth international conference on artificial intelligence and statistics, pp. 661-668. JMLR Workshop and Conference Proceedings, 2010.
• Ross, Stéphane, Geoffrey Gordon, and Drew Bagnell. “A reduction of imitation learning and structured prediction to no-regret online learning.” In Proceedings of the fourteenth international conference on artificial intelligence and statistics, pp. 627-635. JMLR Workshop and Conference Proceedings, 2011.
• Le, Hoang, Nan Jiang, Alekh Agarwal, Miroslav Dudík, Yisong Yue, and Hal Daumé. “Hierarchical imitation and reinforcement learning.” In International Conference on Machine Learning, pp. 2917-2926. PMLR, 2018.

The algorithm implementation is partially based on the work and its repository hierarchical_IL_RL:

• Le, Hoang, Nan Jiang, Alekh Agarwal, Miroslav Dudík, Yisong Yue, and Hal Daumé. “Hierarchical imitation and reinforcement learning.” In International Conference on Machine Learning, pp. 2917-2926. PMLR, 2018.

Regarding Machine Learning for Optimization

The proposed method can also be regarded as one of the three learn-to-optimize paradigms concluded in the following
literature:

• Bengio, Yoshua, Andrea Lodi, and Antoine Prouvost. “Machine learning for combinatorial optimization: a methodological tour d’horizon.” European Journal of Operational Research (2020).

The three learn-to-optimize paradigms are illustrated below, where our method serves as an end-to-end paradigm:

License

Released under the modified BSD license. See LICENSE for more details.