  The task of intercepting a target moving along a rectilinear or circular
trajectory by a Dubins' car is formulated as a time-optimal control problem
with an arbitrary direction of the car's velocity at the interception moment.
To solve this problem and to synthesize interception trajectories, neural
network methods of unsupervised learning based on the Deep Deterministic Policy
Gradient algorithm are used. The analysis of the obtained control laws and
interception trajectories in comparison with the analytical solutions of the
interception problem is performed. The mathematical modeling for the parameters
of the target movement that the neural network had not seen before during
training is carried out. Model experiments are conducted to test the stability
of the neural solution. The effectiveness of using neural network methods for
the synthesis of interception trajectories for given classes of target
movements is shown.
