# Deep Reinforcement Learning for Controlled Traversing of the Attractor Landscape of Boolean Models in the Context of Cellular Reprogramming

arXiv: https://arxiv.org/abs/2402.08491 · HTML: https://mlsanity.com/paper/2402.08491/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 9 of its 32 references that are in the map.
- First posted on arXiv 13 Feb 2024.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 9 of 9)

- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [Action Branching Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1711.08946/) (2017)
- [A Survey of Reinforcement Learning Algorithms for Dynamically Varying Environments](https://mlsanity.com/paper/2005.10619/) (2020)
- [Control of complex networks requires both structure and dynamics](https://mlsanity.com/paper/1509.08409/) (2015)
- [Marker and source-marker reprogramming of Most Permissive Boolean networks and ensembles with BoNesis](https://mlsanity.com/paper/2207.13307/) (2022)
- [Deep Reinforcement Learning for Stabilization of Large-scale Probabilistic Boolean Networks](https://mlsanity.com/paper/2210.12229/) (2022)
- [A Dynamics-based Approach for the Target Control of Boolean Networks](https://mlsanity.com/paper/2006.02304/) (2020)

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

- [Graph Neural Network-Based Reinforcement Learning for Controlling Biological Networks - the GATTACA Framework](https://mlsanity.com/paper/2505.02712/) (2025)

## Abstract (arXiv)

Cellular reprogramming can be used for both the prevention and cure of different diseases. However, the efficiency of discovering reprogramming strategies with classical wet-lab experiments is hindered by lengthy time commitments and high costs. In this study, we develop a novel computational framework based on deep reinforcement learning that facilitates the identification of reprogramming strategies. For this aim, we formulate a control problem in the context of cellular reprogramming for the frameworks of BNs and PBNs under the asynchronous update mode. Furthermore, we introduce the notion of a pseudo-attractor and a procedure for identification of pseudo-attractor state during training. Finally, we devise a computational framework for solving the control problem, which we test on a number of different models.