Acquisition in short steps of adaptive action models of mobile robots for target environments by transfer learning

Abstract

Recently, autonomous mobile robots based on action models trained by deep reinforcement learning have been studied. Generally, it is desirable to train action models in the environment simulating the actual environment. However, it takes many steps to complete training and acquire an action model adaptive to the target environment. A purpose of this study is to acquire an action model adaptive to each environment in short steps by transfer learning from generic action models. The generic action models are acquired by training in environments containing various artificial shapes. An adaptive action models to each target environment can be obtained in shorter steps based on fine tuning of the generic action models.