Modeling Reinforcement Learning Algorithms for performance analysis

Reinforcement Learning Algorithms present interesting learning techniques. Here an autonomous agent interacts with its environment to choose optimal actions to achieve its goals. The performance of an agent is determined by how quickly it learns and converges to an optimal solution. Q-learning and Prioritized sweeping provide interesting techniques to achieve this. In this paper we try to analyze the performance of Q-learning and Prioritized sweeping as examples of model free and model based reinforcement learning. We also try to analyze the optimal number of backups required for prioritized sweeping. We model the results of prioritized sweeping as a regression model and discuss the prediction of the model by comparing it with the accuracy of our simulation results.