# Deep Reinforcement Learning for Stabilization of Large-scale Probabilistic Boolean Networks

arXiv: https://arxiv.org/abs/2210.12229 · HTML: https://mlsanity.com/paper/2210.12229/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 70 references that are in the map.
- First posted on arXiv 21 Oct 2022.

## Builds on (most cited first, 11 of 11)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://mlsanity.com/paper/2106.01345/) (2021)
- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [When to Trust Your Model: Model-Based Policy Optimization](https://mlsanity.com/paper/1906.08253/) (2019)
- [Model-Ensemble Trust-Region Policy Optimization](https://mlsanity.com/paper/1802.10592/) (2018)
- [Deep Reinforcement Learning for Control of Probabilistic Boolean Networks](https://mlsanity.com/paper/1909.03331/) (2019)
- [A Web-based Tool for Identifying Strategic Intervention Points in Complex Systems](https://mlsanity.com/paper/1608.00655/) (2016)
- [A Polynomial-Time Algorithm for Solving the Minimal Observability Problem in Conjunctive Boolean Networks](https://mlsanity.com/paper/1706.04072/) (2017)
- [Distributed Pinning Set Stabilization of Large-Scale Boolean Networks](https://mlsanity.com/paper/2203.07986/) (2022)

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

- [Deep Reinforcement Learning for Controlled Traversing of the Attractor Landscape of Boolean Models in the Context of Cellular Reprogramming](https://mlsanity.com/paper/2402.08491/) (2024)
- [Graph Neural Network-Based Reinforcement Learning for Controlling Biological Networks - the GATTACA Framework](https://mlsanity.com/paper/2505.02712/) (2025)

## Abstract (arXiv)

The ability to direct a Probabilistic Boolean Network (PBN) to a desired state is important to applications such as targeted therapeutics in cancer biology. Reinforcement Learning (RL) has been proposed as a framework that solves a discrete-time optimal control problem cast as a Markov Decision Process. We focus on an integrative framework powered by a model-free deep RL method that can address different flavours of the control problem (e.g., with or without control inputs; attractor state or a subset of the state space as the target domain). The method is agnostic to the distribution of probabilities for the next state, hence it does not use the probability transition matrix. The time complexity is linear on the time steps, or interactions between the agent (deep RL) and the environment (PBN), during training. Indeed, we explore the scalability of the deep RL approach to (set) stabilization of large-scale PBNs and demonstrate successful control on large networks, including a metastatic melanoma PBN with 200 nodes.