Advantage Actor-Critic (A2C)

Advantage Actor-Critic (A2C) is a reinforcement learning algorithm that helps an agent learn to make better decisions. It combines two components: the "actor," which chooses actions based on current knowledge, and the "critic," which evaluates how good those actions are. The critic provides feedback (the “advantage”) that guides the actor to improve its choices. By working together, they efficiently learn optimal strategies in complex environments, balancing exploration and exploitation, leading to faster and more stable learning.