Despite the promising results of reinforcement learning in autonomous maneuvers for ground-based applications, its potential for autonomous target acquisition and collision avoidance in space applications has yet to be thoroughly investigated. Therefore, this study attempts to develop a reinforcement learning-based resilient motion planning approach for autonomous target acquisition and collision avoidance, while focusing on the application where a fying robot is providing assistance to astronauts under microgravity conditions inside the International Space Station. Nevertheless, these complex motion planning strategies require comprehensive validations before deployment on real hardware. Therefore, the study is designed around a ground-based mobile spacecraft platform. The problem at hand has been further simplifed to a 3-Degree of Freedom problem as these types of experiments initially tested and validated in simulated microgravity laboratory environments located on earth. During this research, reinforcement learning agents have been trained to perform tasks, including a) target acquisition in obstacle-free environments, b) target acquisition while avoiding static obstacles, and c) target acquisition while avoiding dynamic obstacles. In all tasks, the trained agents efciently reach the target conditions while successfully avoiding collisions with obstacles.