# Revolutionizing Genomics with Reinforcement Learning Techniques

arXiv: https://arxiv.org/abs/2302.13268 · HTML: https://mlsanity.com/paper/2302.13268/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 137 references that are in the map.
- First posted on arXiv 26 Feb 2023.

## Builds on (most cited first, 18 of 18)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Hindsight Experience Replay](https://mlsanity.com/paper/1707.01495/) (2017)
- [Efficient Neural Architecture Search via Parameter Sharing](https://mlsanity.com/paper/1802.03268/) (2018)
- [Domain Generalization: A Survey](https://mlsanity.com/paper/2103.02503/) (2021)
- [A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress](https://mlsanity.com/paper/1806.06877/) (2018)
- [Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion](https://mlsanity.com/paper/1807.01675/) (2018)
- [Parameter Space Noise for Exploration](https://mlsanity.com/paper/1706.01905/) (2017)
- [Deep Reinforcement Learning for De-Novo Drug Design](https://mlsanity.com/paper/1711.10907/) (2017)
- [Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation](https://mlsanity.com/paper/1807.01473/) (2018)
- [Temporal Difference Models: Model-Free Deep RL for Model-Based Control](https://mlsanity.com/paper/1802.09081/) (2018)
- [Applications of Deep Learning and Reinforcement Learning to Biological Data](https://mlsanity.com/paper/1711.03985/) (2017)
- [On Periodic Reference Tracking Using Batch-Mode Reinforcement Learning with Application to Gene Regulatory Network Control](https://mlsanity.com/paper/1303.2987/) (2013)
- [A reinforcement learning approach to resource allocation in genomic selection](https://mlsanity.com/paper/2107.10901/) (2021)
- [Toggling a Genetic Switch Using Reinforcement Learning](https://mlsanity.com/paper/1303.3183/) (2013)
- [Improving RNA Secondary Structure Design using Deep Reinforcement Learning](https://mlsanity.com/paper/2111.04504/) (2021)
- [A step toward a reinforcement learning de novo genome assembler](https://mlsanity.com/paper/2102.02649/) (2021)
- [Reinforcement Learning for Personalized Drug Discovery and Design for Complex Diseases: A Systems Pharmacology Perspective](https://mlsanity.com/paper/2201.08894/) (2022)
- [CryoRL: Reinforcement Learning Enables Efficient Cryo-EM Data Collection](https://mlsanity.com/paper/2204.07543/) (2022)

## Abstract (arXiv)

In recent years, Reinforcement Learning (RL) has emerged as a powerful tool for solving a wide range of problems, including decision-making and genomics. The exponential growth of raw genomic data over the past two decades has exceeded the capacity of manual analysis, leading to a growing interest in automatic data analysis and processing. RL algorithms are capable of learning from experience with minimal human supervision, making them well-suited for genomic data analysis and interpretation. One of the key benefits of using RL is the reduced cost associated with collecting labeled training data, which is required for supervised learning. While there have been numerous studies examining the applications of Machine Learning (ML) in genomics, this survey focuses exclusively on the use of RL in various genomics research fields, including gene regulatory networks (GRNs), genome assembly, and sequence alignment. We present a comprehensive technical overview of existing studies on the application of RL in genomics, highlighting the strengths and limitations of these approaches. We then discuss potential research directions that are worthy of future exploration, including the development of more sophisticated reward functions as RL heavily depends on the accuracy of the reward function, the integration of RL with other machine learning techniques, and the application of RL to new and emerging areas in genomics research. Finally, we present our findings and conclude by summarizing the current state of the field and the future outlook for RL in genomics.