This study applies deep-reinforcement-learning algorithms to integrated guidance and control for three-dimensional, high-maneuverability missile-target interception. Dynamic environment, reward functions concerning multi-factors, agents based on the deep-deterministic-policy-gradient algorithm, and action signals with pitch and yaw fins as control commands were constructed in the research, which control the missile in order to intercept targets. Firstly, the missile-interception system includes dynamics such as the inertia of the missile, the aerodynamic parameters, and fin delays. Secondly, to improve the convergence speed and guidance accuracy, a convergence factor for the angular velocity of the target line of sight and deep dual-filter methods were introduced into the design of the reward function. The method proposed in this paper was then compared with traditional proportional navigation. Next, many simulations were carried out on high-maneuverability targets with different initial conditions by randomization. The numerical-simulation results showed that the proposed guidance strategy has higher guidance accuracy and stronger robustness and generalization capability against the aerodynamic parameters.