# Reinforcement Learning for the Unit Commitment Problem

arXiv: https://arxiv.org/abs/1507.05268 · HTML: https://mlsanity.com/paper/1507.05268/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2015.

## Built on by (most cited first, 2 of 2)

- [Model-predictive control and reinforcement learning in multi-energy system case studies](https://mlsanity.com/paper/2104.09785/) (2021)
- [An Optimization Method-Assisted Ensemble Deep Reinforcement Learning Algorithm to Solve Unit Commitment Problems](https://mlsanity.com/paper/2206.04249/) (2022)

## Abstract (arXiv)

In this work we solve the day-ahead unit commitment (UC) problem, by formulating it as a Markov decision process (MDP) and finding a low-cost policy for generation scheduling. We present two reinforcement learning algorithms, and devise a third one. We compare our results to previous work that uses simulated annealing (SA), and show a 27% improvement in operation costs, with running time of 2.5 minutes (compared to 2.5 hours of existing state-of-the-art).