  The goal of policy gradient approaches is to find a policy in a given class
of policies which maximizes the expected return. Given a differentiable model
of the policy, we want to apply a gradient-ascent technique to reach a local
optimum. We mainly use gradient ascent, because it is theoretically well
researched. The main issue is that the policy gradient with respect to the
expected return is not available, thus we need to estimate it. As policy
gradient algorithms also tend to require on-policy data for the gradient
estimate, their biggest weakness is sample efficiency. For this reason, most
research is focused on finding algorithms with improved sample efficiency. This
paper provides a formal introduction to policy gradient that shows the
development of policy gradient approaches, and should enable the reader to
follow current research on the topic.
