A Reinforcement Learning Method for Gliding Control of Underwater Gliding Snake-like Robot

A reinforcement learning algorithm for gliding control of underwater gliding snake-like robot is studied. To solve the problem that the hydrodynamic environment is hard to be modeled, a reinforcement learning method is adopted so that the underwater gliding snake-like robot can adapt to the complex water environment and automatically learn the gliding actions only by adjusting buoyancy. A Monte Carlo policy gradient algorithm using recurrent neural network is proposed to solve the problem that the algorithm is difficult to train because the robot state can't be fully observed. The gliding action control of the underwater gliding snake-like robot is approximated as Markov decision processes (MDPs), so as to obtain an effective gliding control policy. Simulation and experiment results show the effectiveness of the proposed method.