Evaluation of Safety Constraints in Autonomous Navigation with Deep
  Reinforcement Learning
Brian Angulo Gregory Gorbov Aleksandr Panov Konstantin Yakovlev
While reinforcement learning algorithms have had great success in the field
of autonomous navigation, they cannot be straightforwardly applied to the real
autonomous systems without considering the safety constraints. The later are
crucial to avoid unsafe behaviors of the autonomous vehicle on the road. To
highlight the importance of these constraints, in this study, we compare two
learnable navigation policies: safe and unsafe. The safe policy takes the
constraints into account, while the other does not. We show that the safe
policy is able to generate trajectories with more clearance (distance to the
obstacles) and makes less collisions while training without sacrificing the
overall performance.