  Reinforcement learning (RL) exhibits impressive performance when managing
complicated control tasks for robots. However, its wide application to physical
robots is limited by the absence of strong safety guarantees. To overcome this
challenge, this paper explores the control Lyapunov barrier function (CLBF) to
analyze the safety and reachability solely based on data without explicitly
employing a dynamic model. We also proposed the Lyapunov barrier actor-critic
(LBAC), a model-free RL algorithm, to search for a controller that satisfies
the data-based approximation of the safety and reachability conditions. The
proposed approach is demonstrated through simulation and real-world robot
control experiments, i.e., a 2D quadrotor navigation task. The experimental
findings reveal this approach's effectiveness in reachability and safety,
surpassing other model-free RL methods.
