Enabling MDP Solution Transfer by Abstraction

We consider the problem of transferring a learned optimal policy between MDPs. We describe a method that views the task of rep-resenting the solution as a supervised learn-ing problem. Our method determines the new abstract policy by Decision Tree learn-ing over values of features on various levels of abstraction. We provide empirical results that show acceleration in learning due to the solution instantiation in the target problem