Advantage Actor Critic

Advantage Actor Critic (A2C) is a reinforcement learning algorithm that combines value-based and policy-based methods to improve stability and efficiency. It uses an actor network to select actions based on a policy and a critic network to estimate the value function, with the advantage function (difference between actual and expected returns) guiding policy updates. This approach reduces variance in training compared to pure policy gradient methods like REINFORCE.

Also known as: A2C, Advantage Actor-Critic, Actor Critic with Advantage, Advantage AC, A2C Algorithm

Why learn Advantage Actor Critic?

Developers should learn A2C when building AI agents for complex environments like robotics, game playing, or autonomous systems, as it offers a balance between exploration and exploitation with faster convergence. It is particularly useful in continuous action spaces or scenarios requiring stable learning, such as training agents in simulation environments like OpenAI Gym or MuJoCo.

Learning Resources

• docs: OpenAI Spinning Up - Advantage Actor Critic
• tutorial: DeepMind's A2C Tutorial
• course: Coursera - Reinforcement Learning Specialization
• video: YouTube - A2C Explained by Sentdex
• book: Book: Reinforcement Learning: An Introduction