A3C (Asynchronous Advantage Actor-Critic) is a fundamental algorithm in Reinforcement Learning, designed to train artificial agents efficiently. It employs multiple worker agents to explore diverse environment copies in parallel, asynchronously updating a shared Neural Network. This parallel, decentralized approach often leads to faster and more stable learning outcomes.