The amphibious hexapod robot is characterized by its flexible legs, which aims to address the terrestrial and underwater motion control in complex environment. In this article, a deep reinforcement learning based terrestrial motion control is firstly proposed for movements on rugged lands. By building agent interaction scenarios using the MuJoCo physical engine, the proximal policy optimization algorithm is employed to obtain the optimal motion policy applied in rugged lands of different conditions. Simulation results show that the robot is capable of climbing fast and stable on rugged terrains when controlled by the generated policies. Concerning the underwater motion control issue, the hydrodynamic model is derived. Based on its analysis, the three-dimensional underwater motions can be decoupled into the planar trajectory tracking and the depth control. Especially, the LOS and PID control are used. Experimental results prove that the robot can track the Sigmoid curve with path error less than 0. 11 m. In addition, the robot is qualified to realize the PID based depth control with the accuracy of 0. 02 m.