Real World Reinforcement Learning
نویسندگان
چکیده
Self-sustenance in an unknown environment for an autonomous mobile robot with restricted sensory faculties is a challenging problem, primarily due to lack of effectiveness of existing techniques (deterministic or learning-based) in real world. Through this thesis, we try to investigate the utility of some of the existing reinforcement learning based techniques in achieving energy self-sufficiency for an autonomous robot MR. ESS (Mobile Robot: Energetically Self-Sufficient) operating in an unknown environment. The problem is made interesting by the fact that the only sensory input available to the robot is the one corresponding to energy level detection, thereby rendering any localisation/mapping based techniques ineffective. In this study, we propose an action selection strategy for the robot based on its ‘familiarity’ with a given state. We contrast this with the probabilistic action selection and also suggest a variant of eligibility trace based Q-learning algorithm. We analyze and interpret both simulation and real world experiment results and argue the (dis)advantages of each approach towards development of self-sustaining robots.
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