Preferential exploration method of transfer learning for reinforcement learning in Same Transition Model

We aim to accelerate learning processes in reinforcement learning by transfer learning. Its concept is that knowledge to solve similar tasks accelerates a learning process of a target task. We have proposed that the basic transfer method based on forbidden rule set that is a set of rules which cause to immediately failure of a target task. However, the basic method works poorly for the "Same Transition Model," which has same state transition probability and different goal. In this article, we propose an effective transfer learning method in same transition model. In detail, it consists of two strategies: (1) approaching to the goal for the selected source task quickly, and (2) exploring states around the goal preferentially.