  This paper gives a detailed review of reinforcement learning (RL) in
combinatorial optimization, introduces the history of combinatorial
optimization starting in the 1950s, and compares it with the RL algorithms of
recent years. This paper explicitly looks at a famous combinatorial
problem-traveling salesperson problem (TSP). It compares the approach of modern
RL algorithms for the TSP with an approach published in the 1970s. By comparing
the similarities and variances between these methodologies, the paper
demonstrates how RL algorithms are optimized due to the evolution of machine
learning techniques and computing power. The paper then briefly introduces the
deep learning approach to the TSP named deep RL, which is an extension of the
traditional mathematical framework. In deep RL, attention and feature encoding
mechanisms are introduced to generate near-optimal solutions. The survey shows
that integrating the deep learning mechanism, such as attention with RL, can
effectively approximate the TSP. The paper also argues that deep learning could
be a generic approach that can be integrated with any traditional RL algorithm
to enhance the outcomes of the TSP.
