# Enhancing Reinforcement learning in 3-Dimensional Hydrophobic-Polar Protein Folding Model with Attention-based layers

arXiv: https://arxiv.org/abs/2504.15634 · HTML: https://mlsanity.com/paper/2504.15634/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 10 of its 28 references that are in the map.
- First posted on arXiv 22 Apr 2025.

## 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, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour](https://mlsanity.com/paper/1706.02677/) (2017)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [Distributed Prioritized Experience Replay](https://mlsanity.com/paper/1803.00933/) (2018)
- [FoldingZero: Protein Folding from Scratch in Hydrophobic-Polar Model](https://mlsanity.com/paper/1812.00967/) (2018)
- [Applying Deep Reinforcement Learning to the HP Model for Protein Structure Prediction](https://mlsanity.com/paper/2211.14939/) (2022)
- [Protein folding simulations in the hydrophobic-polar model using a hybrid cuckoo search algorithm](https://mlsanity.com/paper/2105.13226/) (2021)

## Abstract (arXiv)

Transformer-based architectures have recently propelled advances in sequence modeling across domains, but their application to the hydrophobic-hydrophilic (H-P) model for protein folding remains relatively unexplored. In this work, we adapt a Deep Q-Network (DQN) integrated with attention mechanisms (Transformers) to address the 3D H-P protein folding problem. Our system formulates folding decisions as a self-avoiding walk in a reinforced environment, and employs a specialized reward function based on favorable hydrophobic interactions. To improve performance, the method incorporates validity check including symmetry-breaking constraints, dueling and double Q-learning, and prioritized replay to focus learning on critical transitions. Experimental evaluations on standard benchmark sequences demonstrate that our approach achieves several known best solutions for shorter sequences, and obtains near-optimal results for longer chains. This study underscores the promise of attention-based reinforcement learning for protein folding, and created a prototype of Transformer-based Q-network structure for 3-dimensional lattice models.