2026-04-03T20:16:43Zhttps://recercat.cat/oai/request
oai:recercat.cat:10256/21782024-05-22T09:46:14Zcom_2072_452955com_2072_2054col_2072_452957
RECERCAT author El-Fakdi Sencianes, Andrés author Carreras Pérez, Marc 2024-05-22T09:46:13Z 2024-05-22T09:46:13Z 2008 http://hdl.handle.net/10256/2178 This paper proposes a field application of a high-level reinforcement learning (RL) control system for solving the action selection problem of an autonomous robot in cable tracking task. The learning system is characterized by using a direct policy search method for learning the internal state/action mapping. Policy only algorithms may suffer from long convergence times when dealing with real robotics. In order to speed up the process, the learning phase has been carried out in a simulated environment and, in a second step, the policy has been transferred and tested successfully on a real robot. Future steps plan to continue the learning process on-line while on the real robot while performing the mentioned task. We demonstrate its feasibility with real experiments on the underwater robot ICTINEU AUV Tots els drets reservats info:eu-repo/semantics/openAccess Aprenentatge per reforç Robots autònoms Autonomous robots Reinforcement learning Policy gradient based Reinforcement Learning for real autonomous underwater cable tracking info:eu-repo/semantics/article