Asynchronous Q Learning

Asynchronous Q-learning is a reinforcement learning algorithm focusing on efficient and parallel updates of the Q-function, which estimates the value of taking specific actions in different states. Current research emphasizes improving the convergence properties and theoretical understanding of asynchronous Q-learning, particularly through analyses using ordinary differential equations and Markov chain models, and exploring its application in diverse settings such as quantum reinforcement learning and multi-agent systems. These advancements aim to enhance the algorithm's performance, stability, and applicability to complex real-world problems, including task scheduling and robust decision-making under uncertainty. The development of variance-reduction techniques and the incorporation of pessimism principles further contribute to improved sample efficiency and robustness.