The fundamentals of reinforcement learning are given in chapter \ref{sec:02:basics}. These contain core terms
and definitions required to discuss and construct reinforcement learning algorithms.
Issues with the naive learning approach outlined in chapter \ref{sec:02:basics} are pointed out in chapter
\ref{sec:03:ppo}. This leads to the introduction of advanced estimation methods, which are used in \emph{Proximal Policy
Issues with the naive learning approach outlined in chapter \ref{sec:02:basics} are pointed out in chapter~\ref{sec:03:ppo}. This leads to the introduction of advanced estimation methods, which are used in \emph{Proximal Policy
Optimization}. With these estimation methods PPO is defined and ramifications of specific operations are explained.
Chapter \ref{sec:03:ppo} closes with an outline of the complete reinforcement learning algorithm.