A3C

A3C (Asynchronous Advantage Actor-Critic) is a reinforcement learning algorithm that enables multiple agents to learn simultaneously by exploring different ways to solve a problem. Each agent interacts with its environment, making decisions and collecting feedback, while the central system updates the overall strategy based on their experiences. By running multiple agents in parallel, A3C speeds up learning and improves stability, leading to better performance in complex tasks like playing games or controlling robots. It combines the strengths of policy-based and value-based methods to efficiently learn effective strategies.