Improving the Learning Rate by Inducing a Transition Model

In general, a reinforcement learning agent requires many trials in order to find a successful policy in a domain. In this paper we investigate inducing a transition model to reduce the number of trials required by an agent.We discuss an approach that incorporates transition model learning within a contemporary agent design.