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...
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Published in | 2014 Oceans - St. John's pp. 1 - 6 |
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Main Authors | , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
01.09.2014
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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ISSN: | 0197-7385 |
DOI: | 10.1109/OCEANS.2014.7003088 |