Intelligent Parameter Tuning Using Segmented Recursive Reinforcement Learning Algorithm

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2020 by IJETT Journal
Volume-68 Issue-6
Year of Publication : 2020
Authors : Modalavalasa Hari Krishna, Dr.Makkena Madhavi Latha
DOI :  10.14445/22315381/IJETT-V68I6P201S


MLA Style: Modalavalasa Hari Krishna, Dr.Makkena Madhavi Latha  "Intelligent Parameter Tuning Using Segmented Recursive Reinforcement Learning Algorithm" International Journal of Engineering Trends and Technology 68.6(2020):1-8. 

APA Style:Modalavalasa Hari Krishna, Dr.Makkena Madhavi Latha. Intelligent Parameter Tuning Using Segmented Recursive Reinforcement Learning Algorithm  International Journal of Engineering Trends and Technology, 68(6),1-8.

Now-a-days, Machine Learning plays a vital role in enhancing the capabilities of traditional algorithms and processing techniques to handle profusion of data due to advances in digital technologies. Many processing problems can be solved using optimization-based solutions. In general, these solutions are normalized for different applications. Most of these solutions have control parameters to maintain relative importance to a specific application and also to optimize the performance of the solution. Parameter tuning is a straightforward task by manually determining the direction for adjustment of the parameter. But manual adjustment for these control parameters is tedious and consumes too much time and effort. Manual control becomes impractical if the solution has more parameters and requires precise tuning. With control being complex, machine learning has emerged as a key component in setting up correct and precise parameters. Reinforcement Learning (RL) can solve this problem but existing RL algorithms requires huge amount of learning time and resources. This paper aims at solving this problem and proposes novel Segmented and Recursive Reinforcement Learning (SRRL) algorithm to train the system that can automatically adjust the parameters accurately and precisely with minimal learning time. Performance of the proposing algorithm is validated in wavelet-based noise reduction technique by employing SRRL algorithm to adjust 3 control parameters of Noise based Hybrid Threshold method. After integrating with the proposing SRRL algorithm, the performance of the considered noise reduction technique is improved and provide better PSNR values with minimum learning time than with existing RL algorithms.


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Intelligent Parameter Tuning, Noise based Hybrid Threshold, Machine Learning, Reinforcement Learning, Segmented and Recursive Reinforcement Learning.