Improving the sample efficiency of reinforcement learning algorithms to scale up to larger and more realistic domains is a current research challenge in machine learning. Model-based methods use experiential data more efficiently than model-free approaches but often require exhaustive exploration to learn an accurate model of the domain. We present an algorithm, Reinforcement Learning with Decision Trees (textsc{rl-dt}), that uses supervised learning techniques to learn the model by generalizing the relative effect of actions across states. Specifically, textsc{rl-dt} uses decision trees to model the relative effects of actions in the domain. The agent explores the environment exhaustively in early episodes when its model is inaccurate. Once it believes it has developed an accurate model, it exploits its model, taking the optimal action at each step. The combination of the learning approach with the targeted exploration policy enables fast learning of the model. The sample efficiency of the algorithm is evaluated empirically in comparison to five other algorithms across three domains. textsc{rl-dt} consistently accrues high cumulative rewards in comparison with the other algorithms tested.

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In The Eighth International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2009.

@InProceedings{AAMAS09-hester, title={Generalized Model Learning for Reinforcement Learning in Factored Domains}, author={Todd Hester and Peter Stone}, booktitle={The Eighth International Conference on Autonomous Agents and Multiagent Systems (AAMAS)}, month={May}, url="http://www.cs.utexas.edu/users/ai-lab?AAMAS09-hester", year={2009} }