Applications of Reinforcement Learning in Real World

Data Science, AI and ML
#1

There is no reasoning, no process of inference or comparison; there is no thinking about things, no putting two and two together; there are no ideas — the animal does not think of the box or of the food or of the act he is to perform. — — Edward Thorndike(1874–1949), the psychologist who proposed Law of effect.
While Convolution Neural Network (CNN) and Recurrent Neural Network (RNN) are becoming more important for businesses due to their applications in Computer Vision (CV) and Natural Language Processing (NLP), Reinforcement Learning (RL) as a framework for computational neuroscience to model decision making process seems to be undervalued. Besides, there seems to be very little resources detailing how RL is applied in different industries. Despite the criticisms about RL’s weaknesses, RL should never be neglected in the space of corporate research given its huge potentials in assisting decision making. As Koray Kavukcuoglu, the director of research at Deepmind, said at a conference,
“If one of the goals that we work for here is AI then it is at the core of that. Reinforcement Learning is a very general framework for learning sequential decision making tasks. And Deep Learning, on the other hand, is of course the best set of algorithms we have to learn representations. And combinations of these two different models is the best answer so far we have in terms of learning very good state representations of very challenging tasks that are not just for solving toy domains but actually to solve challenging real world problems.”
Therefore, this article aims to 1)investigate the breadth and depth of RL applications in real world; 2)view RL from different aspects; and 3)persuade the decision makers and researchers to put more efforts on RL research.
The rest of the article is organized as follows. Section I is a general introduction. Section II presents the applications of RL in different domains and a brief description of how it was applied. Section III summarizes the things one would need to apply RL. Section IV is the intuition from other disciplines and Section V is about how RL could be useful in the future. Section VI is conclusion.