A3C (Asynchronous Actor-Critic)

A3C (Asynchronous Actor-Critic) is an advanced reinforcement learning method where multiple agents (actors) learn simultaneously in different environments, sharing their experiences to update a central model. Each agent makes decisions (actions) and learns from the results, improving its policy over time. This parallel approach speeds up learning and helps prevent the model from getting stuck in suboptimal strategies. The “actor” chooses actions, while the “critic” evaluates how good those actions are, guiding improvements. Overall, A3C efficiently trains intelligent systems to navigate complex tasks by leveraging concurrent learning and shared knowledge.