How an Adaptive Learning Rate Benefits Neuro-Fuzzy Reinforcement Learning Systems

Kuremoto, Takashi; Obayashi, Masanao; Kobayashi, Kunikazu; Mabu, Shingo

doi:10.1007/978-3-319-11857-4_37

Cited by 2 publications

(1 citation statement)

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“…In [24], a solution was proposed to solve the problem of managing the balance between exploration and exploitation that was present in [23]. The authors proposed an adaptive learning rate, which uses larger learning rates for less visited states and smaller learning rates for more visited states.…”

Section: Kitanov and Davcevmentioning

confidence: 99%

Auto-Scaling Approach for Cloud based Mobile Learning Applications

Almutlaq¹,

Daadaa²

2019

ijacsa

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In the last decade, mobile learning applications have attracted a significant amount of attention. Huge investments have been made to develop educational applications that can be implemented on mobile devices. However, mobile learning applications have some limitations, such as storage space and battery life. Cloud computing provides a new idea to solve some limitations of mobile learning applications. However, there are other limitations, like scalability, that must be solved before mobile cloud learning can become completely operational. There are two main problems with scalability. The first occurs when the application server's performance declines due to an increase in the number of requests, which affects usability. The second is that a decrease in the number of requests makes most application servers idle and therefore wastes money. These two problems can be avoided or minimized by provisioning autoscaling techniques that permit the acquisition and release of resources dynamically to accommodate demand. In this paper, we propose an intelligent neuro-fuzzy reinforcement learning approach to solve the scalability problem in mobile cloud learning applications, and evaluate the proposed approach against some of the existing approaches via MATLAB. The large state space and long training time required to find the optimal policy are the main problems of reinforcement learning. We use fuzzy Q-learning to solve the large state space problem by grouping similar variables in the same state; there is then no need to use large look-up tables. The use of parallel learning agents reduces the training time needed to determine optimal policies. The experimental results prove that the proposed approach is able to increase learning speed and reduce the training time needed to determine optimal policies.

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Section: Kitanov and Davcevmentioning

confidence: 99%