  Reinforcement Learning is an area of Machine Learning focused on how agents
can be trained to make sequential decisions, and achieve a particular goal
within an arbitrary environment. While learning, they repeatedly take actions
based on their observation of the environment, and receive appropriate rewards
which define the objective. This experience is then used to progressively
improve the policy controlling the agent's behavior, typically represented by a
neural network. This trained module can then be reused for similar problems,
which makes this approach promising for the animation of autonomous, yet
reactive characters in simulators, video games or virtual reality environments.
This paper surveys the modern Deep Reinforcement Learning methods and discusses
their possible applications in Character Animation, from skeletal control of a
single, physically-based character to navigation controllers for individual
agents and virtual crowds. It also describes the practical side of training DRL
systems, comparing the different frameworks available to build such agents.
