A3C, or Asynchronous Actor-Critic Agents, is a reinforcement learning algorithm that enables multiple agents to learn simultaneously in a shared environment. This approach allows for faster training and improved performance by leveraging the experiences of various agents, which can explore different strategies and share their findings. In A3C, each agent operates independently while updating a global model. This model is used to optimize decision-making based on the rewards received from the environment. The method is particularly effective in complex tasks, such as those found in video games or robotics, where diverse strategies can lead to better overall outcomes.