  The issue of the relaxation to equilibrium has been at the core of the
kinetic theory of rarefied gas dynamics. In the paper, we introduce the Deep
Neural Network (DNN) approximated solutions to the kinetic Fokker-Planck
equation in a bounded interval and study the large-time asymptotic behavior of
the solutions and other physically relevant macroscopic quantities. We impose
the varied types of boundary conditions including the inflow-type and the
reflection-type boundaries as well as the varied diffusion and friction
coefficients and study the boundary effects on the asymptotic behaviors. These
include the predictions on the large-time behaviors of the pointwise values of
the particle distribution and the macroscopic physical quantities including the
total kinetic energy, the entropy, and the free energy. We also provide the
theoretical supports for the pointwise convergence of the neural network
solutions to the \textit{a priori} analytic solutions. We use the library
\textit{PyTorch}, the activation function \textit{tanh} between layers, and the
\textit{Adam} optimizer for the Deep Learning algorithm.
