  Reinforcement Learning (RL) algorithms show amazing performance in recent
years, but placing RL in real-world applications such as self-driven vehicles
may suffer safety problems. A self-driven vehicle moving to a target position
following a learned policy may suffer a vehicle with unpredictable aggressive
behaviors or even being pursued by a vehicle following a Nash strategy. To
address the safety issue of the self-driven vehicle in this scenario, this
paper conducts a preliminary study based on a system of robots. A safe RL
framework with safety guarantees is developed for a robot being pursued but
with objectives covering more than capture-avoidance. Simulations and
experiments are conducted based on the system of robots to evaluate the
effectiveness of the developed safe RL framework.
