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.
|Collections||ANU Research Publications|
|Source:||Proceedings of the Third International Joint Conference on Autonomous Agents & Multi Agent Systems (AAMAS 2004)|