Deep Reinforcement Learning

A type of neural network that learns by interacting with the environment through observations, actions, and rewards. Deep reinforcement learning (DRL) has been used to learn gaming strategies, such as Atari and Go - including the famous AlphaGo program that beat a human champion.

Comparison of Deep Reinforcement Learning and Reinforcement Learning

The Reinforcement learning has a major disadvantage that learning the problem spaces are very few and possible states in an environment are only a few. The presence of hidden layer in Deep Reinforcement learning neglect this disadvantage.

Deep Reinforcement Learning Uses

DRL is the most general purpose of all learning techniques so that it can be used in most business applications. It requires less data than other techniques to train its models. Even more notable is the fact that it can be trained via simulation, which eliminates the need for labeled data entirely. Given these advantages, expect to see more business applications that combine DRL and agent-based simulation in the coming year.