Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
495280 | Applied Soft Computing | 2015 | 11 Pages |
Abstract
- A new state space representation of the protein folding problem in 2D-HP model is proposed for the use of reinforcement learning methods.
- The proposed state space representation reduces the dependency of the size of the state-action space to the amino acid sequence length.
- The proposed state space representation also provides an actual learning for an agent. Thus, at the end of a learning process an agent could find the optimum fold of any sequence of a certain length, which is not the case in the existing reinforcement learning methods.
- By using the Ant-Q algorithm (an ant based reinforcement learning method), optimum fold of a protein sequence is found rapidly when compared to the standard Q-learning algorithm.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science Applications
Authors
Berat DoÄan, Tamer Ãlmez,