Two-step gradient-based reinforcement learning for underwater robotics behavior learning

This article proposes a field application of a Reinforcement Learning (RL) control system for solving the action selection problem of an autonomous robot in a cable tracking task. The Ictineu Autonomous Underwater Vehicle (AUV) learns to perform a visual based cable tracking task in a two step learning process. First, a policy is computed by means of simulation where a hydrodynamic model of the vehicle simulates the cable following task. The identification procedure follows a specially designed Least Squares (LS) technique. Once the simulated results are accurate enough, in a second step, the learnt-in-simulation policy is transferred to the vehicle where the learning procedure continues in a real environment, improving the initial policy. The Natural Actor–Critic (NAC) algorithm has been selected to solve the problem. This Actor–Critic (AC) algorithm aims to take advantage of Policy Gradient (PG) and Value Function (VF) techniques for fast convergence. The work presented contains extensive real experimentation. The main objective of this work is to demonstrate the feasibility of RL techniques to learn autonomous underwater tasks, the selection of a cable tracking task is motivated by an increasing industrial demand in a technology to survey and maintain underwater structures.