Deep Reinforcement Learning

What is deep reinforcement learning?

Deep reinforcement learning is a branch of machine learning that is concerned with teaching agents to take action in an environment in order to maximize a reward. The key difference between deep reinforcement learning and other types of machine learning is that deep reinforcement learning involves a process of trial and error, where the agent learns from its mistakes in order to optimize its behavior. In many ways, deep reinforcement learning is similar to the process of learning that humans undergo. For instance, when we are first learning to drive a car, we make mistakes and have accidents. However, with time and experience, we learn how to avoid accidents and become better drivers. In the same way, deep reinforcement learning allows agents to gradually improve their behavior as they gain experience. One of the major benefits of deep reinforcement learning is that it can be used to solve complex tasks that are difficult for other machine learning algorithms. For instance, recent advancements in deep reinforcement learning have been used to teach agents how to play games such as chess and Go. As deep reinforcement learning continues to be developed, it is likely that it will be used to solve ever more complex tasks.