Natarajan, S.S.NatarajanOdom, P.P.OdomJoshi, S.S.JoshiKhot, T.T.KhotKersting, KristianKristianKerstingTadepalli, P.P.Tadepalli2022-03-122022-03-122014https://publica.fraunhofer.de/handle/publica/38742110.1007/978-3-662-44923-3_5The problem of learning to mimic a human expert/teacher from training trajectories is called imitation learning. Tomake the process of teaching easier in this setting, we propose to employ transfer learning (where one learns on a source problem and transfers the knowledge to potentially more complex target problems). We consider multi-relational environments such as real-time strategy games and use functional-gradient boosting to capture and transfer the models learned in these environments. Our experiments demonstrate that our learner learns a very good initial model from the simple scenario and effectively transfers the knowledge to the more complex scenario thus achieving a jump start, a steeper learning curve and a higher convergence in performance.en005conference paper