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This paper investigates learning approaches for discovering fault-tolerant control policies to overcome thruster failures in Autonomous Underwater Vehicles (AUV). The proposed approach is a model-based direct policy search that learns on an on-board simulated model of the vehicle. When a fault is detected and isolated the model of the AUV is reconfigured according to the new condition. To discover a set of optimal solutions a multi-objective reinforcement learning approach is employed which candoi:10.1109/adprl.2014.7010621 dblp:conf/adprl/AhmadzadehKC14 fatcat:mqpv6rud6jan7dxboqkxm6cb3e