Regularization of Soft Actor-Critic Algorithms with Automatic Temperature Adjustment

The SAC algorithm has demonstrated strong performance on a wide range of reinforcement learning tasks, including robotic control, continuous locomotion, and manipulation tasks. It achieves state-of-the-art performance and is known for its stability, sample efficiency, and ability to handle high-dimensional continuous action spaces. However, as with any algorithm, the performance of SAC can be influenced by the choice of hyperparameters, network architecture, and the complexity of the task at hand. Since the introduction of the automatic temperature version of the SAC algorithm came after the fixed temperature version, there may be some ambiguity in the development of the theory, particularly in the derivation of the recursive definition of the soft-Q function.