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ATAL
2009
Springer

Generalized model learning for reinforcement learning in factored domains

10 years 5 months ago
Generalized model learning for reinforcement learning in factored domains
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 modelfree 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 algorit...
Todd Hester, Peter Stone
Added 26 May 2010
Updated 26 May 2010
Type Conference
Year 2009
Where ATAL
Authors Todd Hester, Peter Stone
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