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Prototype selection using reinforcement learning and minimal consistent subset identification guide
Author(s)
Kruatrachue, Boontee
Choowong, Teeratorn
Date Issued
January 1, 2010
Type
Conference Paper
Abstract
This paper try to apply Reinforcement Learning (RL) to a task with large number of states. This usually is a difficult task since RL has less chance to visit all state or has enough number of visit to learn average reward accurately. Moreover, RL may not be able to learn or obtain any optimal solution as RL learn by averaging rewards from each action performing in each state. In order to alleviate this RL learning problem, any solution to a task such as, non-optimal algorithm or heuristics can collaborate with RL by using their knowledge to prune the non-optimal action in each state. This reduces search space of RL and helps it learn faster. A Minimal consistent subset problem is used as an example to demonstrate how RL can learn faster with the help of other heuristics. ©ICROS.
Citation
Iccas 2010 International Conference on Control Automation and Systems, 2320-2323, 2010