A. Fachantidis, I. Partalas, G. Tsoumakas, I. Vlahavas, “Transferring Models in Hybrid Reinforcement Learning Agents”, Proc. Engineering Applications of Neural Networks, Springer Berlin Heidelberg, pp. 162-171, 2011.
Author(s): A. Fachantidis, I. Partalas, Grigorios Tsoumakas, I. Vlahavas
Appeared In: Proc. Engineering Applications of Neural Networks, Springer Berlin Heidelberg, pp. 162-171, 2011.
Abstract: The main objective of transfer learning is to reuse knowledge acquired in a previous learned task, in order to enhance the learning procedure in a new and more complex task. Transfer learning comprises a suitable solution for speeding up the learning procedure in Reinforcement Learning tasks. In this work, we propose a novel method for transferring models to a hybrid reinforcement learning agent. The models of the transition and reward functions of a source task, will be transferred to a relevant but different target task. The learning algorithm of the target task's agent takes a hybrid approach, implementing both model-free and model-based learning, in order to fully exploit the presence of a model. The empirical evaluation, of the proposed approach, demonstrated significant results and performance improvements in the 3D Mountain Car task, by successfully using the models generated from the standard 2D Mountain Car.