Evaluation of Q-learning for search and inspect missions using underwater vehicles

An application for offline Reinforcement Learning in the underwater domain is proposed. We present and evaluate the integration of the Q-learning algorithm into an Autonomous Underwater Vehicle (AUV) for learning the action-value function in simulation. Three separate experiments are presented. The first compares two search policies: the ε — least visited, and random action, with respect to convergence time. The second experiment presents the effect of the learning discount factor, gamma, on the convergence time of the ε — least visited search policy. The final experiment is to validate the use of a policy learnt offline on a real AUV. This learning phase occurs offline within the continuous simulation environment which had been discretized into a grid-world learning problem. Presented results show the system's convergence to a global optimal solution whilst following both sub-optimal policies during simulation. Future work is introduced, after discussion of our results, to enable the system to be used in a real world application. The results presented, therefore, form the basis for future comparative analysis of the necessary improvements such as function approximation of the state space.