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Subject: search.filters.subject.Nearest neighbor classification

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    Prototype selection using reinforcement learning and minimal consistent subset identification guide
    (2010-01-01)
    Kruatrachue, Boontee
    ;
    Choowong, Teeratorn
    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.
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    Episode continuation and exploration start for reinforcement learning with large number of states
    (2009-01-01)
    Kruatrachue, Boontee
    ;
    Anantapornkit, Ekaphol
    This paper presents simple techniques to improve the learning rate of the RL algorithm in a task with large number of states that the algorithm cannot fully explore by going through all possible states. The problem with the standard RL algorithm is that varying the ε parameter in the ε-greedy policy is not sufficient to improve its learning performance. The techniques allow the algorithm to focus on exploring the local path to eventually obtain more rewards and occasionally switching to another path to avoid being trapped in the local region of the search space. In order to investigate the effectiveness of the techniques, the minimal consistent subset identification (MCSI) problem is used as a test problem. The paper concludes by comparing the size of the identified subset obtained from the standard RL algorithm and the proposed algorithm along with those of the standard MCSI method.