# Parallel Automatic History Matching Algorithm Using Reinforcement Learning

arXiv: https://arxiv.org/abs/2211.07434 · HTML: https://mlsanity.com/paper/2211.07434/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 58 references that are in the map.
- First posted on arXiv 14 Nov 2022.

## Builds on (most cited first, 7 of 7)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms](https://mlsanity.com/paper/1911.10635/) (2019)
- [Time Limits in Reinforcement Learning](https://mlsanity.com/paper/1712.00378/) (2017)
- [Towards Better Shale Gas Production Forecasting Using Transfer Learning](https://mlsanity.com/paper/2106.11051/) (2021)
- [A Concise Introduction to Reinforcement Learning in Robotics](https://mlsanity.com/paper/2210.07397/) (2022)

## Abstract (arXiv)

Reformulating the history matching problem from a least-square mathematical optimization problem into a Markov Decision Process introduces a method in which reinforcement learning can be utilized to solve the problem. This method provides a mechanism where an artificial deep neural network agent can interact with the reservoir simulator and find multiple different solutions to the problem. Such formulation allows for solving the problem in parallel by launching multiple concurrent environments enabling the agent to learn simultaneously from all the environments at once, achieving significant speed up.