Asynchronous Reinforcement Learning

Asynchronous Reinforcement Learning (ARL) is a method where multiple agents or processes learn from their experiences simultaneously, rather than one at a time. Imagine a classroom where students learn different subjects at their own pace while sharing insights. In ARL, each agent explores the environment and receives feedback on its actions independently. The results are then pooled together, helping all agents improve faster. This approach speeds up learning by allowing diverse strategies to be tested in parallel, leading to better overall performance and more efficient training in complex tasks or environments.