Technical Note: \cal Q-Learning
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Machine Learning
The MAXQ Method for Hierarchical Reinforcement Learning
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Learning the structure of Factored Markov Decision Processes in reinforcement learning problems
ICML '06 Proceedings of the 23rd international conference on Machine learning
Model-based function approximation in reinforcement learning
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Efficient reinforcement learning with relocatable action models
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Efficient structure learning in factored-state MDPs
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
R-MAX: a general polynomial time algorithm for near-optimal reinforcement learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
High-level reinforcement learning in strategy games
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
How to design agent-based simulation models using agent learning
Proceedings of the Winter Simulation Conference
Behavior Abstraction Robustness in Agent Modeling
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
A Tensor Factorization Approach to Generalization in Multi-agent Reinforcement Learning
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
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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 (rl-dt), that uses supervised learning techniques to learn the model by generalizing the relative effect of actions across states. Specifically, 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. rl-dt consistently accrues high cumulative rewards in comparison with the other algorithms tested.