Soft Actor-Critic

Soft Actor-Critic (SAC) is an advanced reinforcement learning algorithm designed to help computers learn how to make optimal decisions by balancing exploration and exploitation. It uses a principle called "maximum entropy," encouraging the system not only to choose good actions but also to explore diverse options. This results in more stable learning and better performance in complex environments. Essentially, SAC trains an agent to act intelligently while maintaining a degree of randomness to discover new strategies, leading to more robust and efficient decision-making processes in tasks like robotics, game playing, or autonomous systems.