This paper presents a deep reinforcement learning (DRL)-based motion control method to provide unmanned aerial vehicles (UAVs) with additional flexibility while flying across dy-namic unknown environments autonomously. This method is ap-plicable in both military and civilian fields such as penetration and rescue. The autonomous motion control problem is ad-dressed through motion planning, action interpretation, traject-ory tracking, and vehicle movement within the DRL framework. Novel DRL algorithms are presented by combining two differ-ence-amplifying approaches with traditional DRL methods and are used for solving the motion planning problem. An improved Lyapunov guidance vector field (LGVF) method is used to handle the trajectory-tracking problem and provide guidance control commands for the UAV. In contrast to conventional motion-con-trol approaches, the proposed methods directly map the sensor-based detections and measurements into control signals for the inner loop of the UAV, i.e., an end-to-end control. The training experiment results show that the novel DRL algorithms provide more than a 20％ performance improvement over the state-of-the-art DRL algorithms. The testing experiment results demon-strate that the controller based on the novel DRL and LGVF, which is only trained once in a static environment, enables the UAV to fly autonomously in various dynamic unknown environ-ments. Thus, the proposed technique provides strong flexibility for the controller.