Advantage Actor-Critic

Advantage Actor-Critic (A2C) is a reinforcement learning method that trains an agent to make decisions by combining two components: the "actor," which chooses actions, and the "critic," which evaluates how good those actions are. The critic assesses the value of states to guide the actor towards better decisions, while the "advantage" helps determine how much better a specific action is compared to the average. This coordinated approach allows the agent to learn efficient strategies in complex environments by continuously improving its decision-making based on feedback from its actions.