Hierarchical extreme learning machine based reinforcement learning for goal localization
The objective of goal localization is to find the location of goals in noisy environments. Simple actions are performed to move the agent towards the goal. The goal detector should be capable of minimizing the error between the predicted locations and the true ones. Few regions need to be process...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
IOP Publishing
2017
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/54838/ http://irep.iium.edu.my/54838/ http://irep.iium.edu.my/54838/ http://irep.iium.edu.my/54838/2/54838-edited.pdf http://irep.iium.edu.my/54838/1/54838-Hierarchical%20extreme%20learning%20machine%20based%20reinforcement%20learning%20for%20goal%20localization_SCOPUS.pdf |
| Summary: | The objective of goal localization is to find the location of goals in noisy
environments. Simple actions are performed to move the agent towards the goal. The goal
detector should be capable of minimizing the error between the predicted locations and the true
ones. Few regions need to be processed by the agent to reduce the computational effort and
increase the speed of convergence. In this paper, reinforcement learning (RL) method was
utilized to find optimal series of actions to localize the goal region. The visual data, a set of
images, is high dimensional unstructured data and needs to be represented efficiently to get a
robust detector. Different deep Reinforcement models have already been used to localize a goal
but most of them take long time to learn the model. This long learning time results from the
weights fine tuning stage that is applied iteratively to find an accurate model. Hierarchical
Extreme Learning Machine (H-ELM) was used as a fast deep model that doesn’t fine tune the
weights. In other words, hidden weights are generated randomly and output weights are
calculated analytically. H-ELM algorithm was used in this work to find good features for
effective representation. This paper proposes a combination of Hierarchical Extreme learning
machine and Reinforcement learning to find an optimal policy directly from visual input. This
combination outperforms other methods in terms of accuracy and learning speed. The
simulations and results were analysed by using MATLAB. |
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