Model Free Algorithm

Model-free algorithms in reinforcement learning aim to learn optimal policies directly from experience without explicitly modeling the environment's dynamics. Current research focuses on improving the sample efficiency and asymptotic performance of these algorithms, exploring techniques like optimistic Q-learning, entropy regularization, and ensemble methods within various architectures such as soft actor-critic and variants of Q-learning. These advancements are significant because they enable efficient learning in complex, high-dimensional settings, with applications ranging from robotics and game playing to resource management and personalized medicine.