Modelling resource allocation in uncertain system environment through
deep reinforcement learning
- URL: http://arxiv.org/abs/2106.09461v1
- Date: Thu, 17 Jun 2021 13:13:34 GMT
- Title: Modelling resource allocation in uncertain system environment through
deep reinforcement learning
- Authors: Neel Gandhi, Shakti Mishra
- Abstract summary: Reinforcement Learning has applications in field of mechatronics, robotics, and other resource-constrained control system.
The paper identifies problem of resource allocation in uncertain environment could be effectively solved using Noisy Bagging duelling double deep Q network.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Reinforcement Learning has applications in field of mechatronics, robotics,
and other resource-constrained control system. Problem of resource allocation
is primarily solved using traditional predefined techniques and modern deep
learning methods. The drawback of predefined and most deep learning methods for
resource allocation is failing to meet the requirements in cases of uncertain
system environment. We can approach problem of resource allocation in uncertain
system environment alongside following certain criteria using deep
reinforcement learning. Also, reinforcement learning has ability for adapting
to new uncertain environment for prolonged period of time. The paper provides a
detailed comparative analysis on various deep reinforcement learning methods by
applying different components to modify architecture of reinforcement learning
with use of noisy layers, prioritized replay, bagging, duelling networks, and
other related combination to obtain improvement in terms of performance and
reduction of computational cost. The paper identifies problem of resource
allocation in uncertain environment could be effectively solved using Noisy
Bagging duelling double deep Q network achieving efficiency of 97.7% by
maximizing reward with significant exploration in given simulated environment
for resource allocation.
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