We all know that learning a new craft is hard. We spend a large part of our lives learning how to operate in everyday physics.  A large part of this learning comes from observing others, and when others can’t help we learn through trial and error. 

In machine learning the process of learning how to deal with the environment is called Reinforcement Learning (RL). By continuous interaction with its environment, an agent learns a policy that enables it to perform better. Observational learning in RL is referred to as Imitation Learning. Both trial and error and imitation learning are hard: environments are not trivial, often you can’t tell the ramifications of an action until far in the future, environments are full of non-determinism and there are no such thing as a correct policy. 

So, unlike in supervised and unsupervised learning, it is hard to tell if your decisions are correct. Episodes usually constitute thousands of decisions, and you will only know if you perform well after exploring other options. But experiment is also a hard decision: do you exploit the skill you already have, or try something new and explore the unknown?

Despite all these complexities, RL has managed to achieve incredible performance in a wide variety of tasks from robotics through recommender systems to trading. More impressively, RL agents have achieved superhuman performance in Go and other games, tasks previously believed to be impossible for computers.