A Comparative Study of Deterministic and Stochastic Policies for Q-learning

Q-learning is a form of reinforcement learning that employs agents to perform actions in an environment under a policy to reach ultimate goals. Q-learning is also thought as a goal-directed learning to maximize the expected value of the cumulative rewards via optimizing policies. Deterministic and scholastic policies are commonly used in reinforcement learning. However, they perform quite different in Markov decision processes. In this study, we conduct a comparative study on these two policies in the context of a grid world problem with Q-learning and provide an insight into the superiority of the deterministic policy over the scholastic one.