Advantage Actor Critic (A2C)

This post introduces Advantage Actor Critic (A2C), a hybrid method combining policy-based and value-based approaches to reduce variance in policy gradient estimation. It details the Actor-Critic architecture, where an Actor controls the agent's behavior and a Critic evaluates actions. The post explains the training process, including how the Actor and Critic are optimized using Q-values and state-action pairs, and demonstrates A2C implementation with PyBullet for robotics simulations.