A2C
A2C
**A2C**, or **Advantage Actor Critic**, is a synchronous version of the [A3C](https://paperswithcode.com/method/a3c) policy gradient method. As an alternative to the asynchronous implementation of A3C, A2C is a synchronous, deterministic implementation that waits for each actor to finish its segment of experience before updating, averaging over all of the actors. This more effectively uses GPUs due to larger batch sizes.

Image Credit: [OpenAI Baselines](https://openai.com/blog/baselines-acktr-a2c/)
Asynchronous Methods for Deep Reinforcement Learning
https://paperswithcode.com/paper/asynchronous-methods-for-deep-reinforcement
Asynchronous Methods for Deep Reinforcement Learning