Deep reinforcement learning algorithm A2C is a variant of policy gradient methods that uses an actor-critic approach to balance exploration and exploitation. It combines the benefits of policy gradients with the value function estimates from Q-learning or SARSA, allowing for more efficient learning in complex environments. --- Why it matters: A2C matters because it provides a practical solution for training agents in complex tasks, such as game playing or robotics, where traditional reinforcement learning methods often struggle to converge. Source: https://huggingface.co/blog/deep-rl-a2c