A3C (Asynchronous Actor-Critic Agents)

A3C, or Asynchronous Actor-Critic Agents, is a method in artificial intelligence for training algorithms to make decisions, especially in complex environments like games. It uses multiple agents (or "actors") that explore different parts of the environment simultaneously, learning from their experiences. Each agent improves its strategy (the "actor" part) while also evaluating the effectiveness of actions (the "critic" part). By working together but independently, these agents can learn faster and more effectively, leading to smarter decision-making overall. A3C enhances training efficiency and allows for better performance in dynamic situations.