Chapter 10. Reinforcement Learning

Reinforcement learning (RL) is an area of machine learning that studies the science of decision-making processes, in particular trying to understand what the best way is to make decisions in a given context. The learning paradigm of RL algorithms is different from most common methodologies, such as supervised or unsupervised learning.

In RL, an agent is programmed as if he were a human being who must learn through a trial and error mechanism in order to find the best strategy to achieve the best result in terms of long-term reward.

RL has achieved incredible results within games (digital and table) and automated robot control, so it is still widely studied. In the last decade, it has been decided to add a key component to RL: neural networks.

This integration of RL and deep neural networks (DNNs), called deep reinforcement learning, has enabled Google DeepMind researchers to achieve amazing results in previously unexplored areas. In particular, in 2013, the...