A First look at Reinforcement Learning

One of the types of learning that we hear about in Machine Learning is Reinforcement Learning, where an agent learns a goal in an environment, known or unknown, through reward and punishment. Unlike learning methods such as Supervised and Unsupervised learning, Reinforcement learning does not require data at all. In my class CS4100, the course briefly touched on the practices of this learning method so I wanted to explore a bit further. A lot of the applications of Reinforcement learning are in games or complex and computationally expensive real-world problems, so it is hard to find something to "meaningfully" apply reinforcement learning to. Nonetheless, this is still a super interesting topic where an agent can build a policy that maximizes the reward function without knowing its environment.