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ATAL
2008
Springer

Expediting RL by using graphical structures

8 years 4 months ago
Expediting RL by using graphical structures
The goal of Reinforcement learning (RL) is to maximize reward (minimize cost) in a Markov decision process (MDP) without knowing the underlying model a priori. RL algorithms tend to be much slower than planning algorithms, which require the model as input. Recent results demonstrate that MDP planning can be expedited, by exploiting the graphical structure of the MDP. We present extensions to two popular RL algorithms, Q-learning and RMax, that learn and exploit the graphical structure of problems to improve overall learning speed. Use of the graphical structure of the underlying MDP can greatly improve the speed of planning algorithms, if the underlying MDP has a nontrivial topological structure. Our experiments show that use of the apparent topological structure of an MDP speeds up reinforcement learning, even if the MDP is simply connected.
Peng Dai, Alexander L. Strehl, Judy Goldsmith
Added 12 Oct 2010
Updated 12 Oct 2010
Type Conference
Year 2008
Where ATAL
Authors Peng Dai, Alexander L. Strehl, Judy Goldsmith
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