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Simultaneous Learning of Objective Function and Policy from Interactive Teaching with Corrective Feedback
2019
2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)
Some imitation learning approaches rely on Inverse Reinforcement Learning (IRL) methods, to decode and generalize implicit goals given by expert demonstrations. The study of IRL normally has the assumption of available expert demonstrations, which is not always possible. There are Machine Learning methods that allow non-expert teachers to guide robots to learn complex policies, which eventually fills the expert dependencies of IRL. This work introduces an approach for simultaneously teaching
doi:10.1109/aim.2019.8868805
dblp:conf/aimech/CeleminK19
fatcat:x7dhf4rtaffefk4waqnpdlibxe