Improving Credit Assignment in a learning classier system with Markov reinforcement learning for protein secondary structure prediction

This research aims to enhance the accuracy of credit assignment for rules in a learning classifier system using Markov Reinforcement Learning for predicting the secondary structure of proteins. Markov Reinforcement Learning has replaced the Bucket Brigade algorithm in the learning classifier system. The Protein Data Bank dataset is utilized to train the system, specifically the protein 4L1W with 5741 samples, where 70% is used for training and 30% for testing purposes. Following the system's training, a set of valuable classifiers (rules) is generated, which will be employed in the testing phase to predict the protein's secondary structure. The experimental results demonstrate an improvement in the accuracy of the Markov Reinforcement Learning classifier system, both in the Learning classifier system and the eXtended classifier system. Through Markov Reinforcement Learning, the credit assignment to each rule is enhanced, resulting in an accuracy improvement of 82.5% for the Learning classifier system and 85% for the eXtended classifier system.