A novel state space representation for the solution of 2D-HP protein folding problem using reinforcement learning methods

- 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.