The A3C network came as a storm and took over the DQN. Aside of the previously stated advantages, it also yields good accuracy compared to other algorithms. It works well in both continuous and discrete action spaces. It uses several agents, and each agent learns in parallel with a different exploration policy in copies of the actual environment. Then, the experience obtained from these agents is aggregated to the global agent. The global agent is also called a master network or global network and other agents are also called the workers. Now, we will see in detail how A3C works and how it differs from the DQN algorithm.