# Hybrid Imitation Learning for Real-Time Service Restoration in Resilient Distribution Systems

arXiv: https://arxiv.org/abs/2011.14458 · HTML: https://mlsanity.com/paper/2011.14458/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Decision transformers and sequence modeling, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 6 of its 49 references that are in the map.
- First posted on arXiv 29 Nov 2020.

## Placed in

- Offline RL and imitation learning from data / [Decision transformers and sequence modeling](https://mlsanity.com/offlinerl/decision-transformers-and-sequence-modeling/)

## Builds on (most cited first, 6 of 6)

- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Hierarchical Imitation and Reinforcement Learning](https://mlsanity.com/paper/1803.00590/) (2018)
- [Learning to Search Better Than Your Teacher](https://mlsanity.com/paper/1502.02206/) (2015)
- [Batch-Constrained Reinforcement Learning for Dynamic Distribution Network Reconfiguration](https://mlsanity.com/paper/2006.12749/) (2020)
- [Markov Decision Process-based Resilience Enhancement for Distribution Systems: An Approximate Dynamic Programming Approach](https://mlsanity.com/paper/1904.00627/) (2019)
- [A Multi-Step Reconfiguration Model for Active Distribution Network Restoration Integrating DG Start-Up Sequences](https://mlsanity.com/paper/2002.08296/) (2020)

## Built on by (most cited first, 1 of 1)

- [LLM-Enhanced Multi-Agent Reinforcement Learning with Expert Workflow for Real-Time P2P Energy Trading](https://mlsanity.com/paper/2507.14995/) (2025)

## Abstract (arXiv)

Self-healing capability is one of the most critical factors for a resilient distribution system, which requires intelligent agents to automatically perform restorative actions online, including network reconfiguration and reactive power dispatch. These agents should be equipped with a predesigned decision policy to meet real-time requirements and handle highly complex N-k scenarios. The disturbance randomness hampers the application of exploration-dominant algorithms like traditional reinforcement learning (RL), and the agent training problem under N-k scenarios has not been thoroughly solved. In this paper, we propose the imitation learning (IL) framework to train such policies, where the agent will interact with an expert to learn its optimal policy, and therefore significantly improve the training efficiency compared with the RL methods. To handle tie-line operations and reactive power dispatch simultaneously, we design a hybrid policy network for such a discrete-continuous hybrid action space. We employ the 33-node system under N-k disturbances to verify the proposed framework.