SAC (Soft Actor-Critic)

Soft Actor-Critic (SAC) is a reinforcement learning algorithm designed to teach machines to make decisions efficiently. It balances exploring new actions with exploiting known rewards by maximizing a combination of expected rewards and a measure called "entropy," which encourages diverse strategies. This approach helps the system learn stable, robust policies for complex tasks, such as robotics or game playing, even in uncertain environments. Essentially, SAC enables an agent to learn optimal actions while maintaining flexibility, leading to better performance and faster learning in dynamic situations.