scholarly journals Online Self-Organizing Network Control with Time Averaged Weighted Throughput Objective

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Zhicong Zhang ◽  
Shuai Li ◽  
Xiaohui Yan
Keyword(s):  
Reinforcement Learning ◽  
Control Problem ◽  
Decision Model ◽  
Learning Algorithm ◽  
Queueing Network ◽  
Network Control ◽  
Learning Ability ◽  
Control Decision ◽  
Self Organizing ◽  
Reinforcement Learning Algorithm

We study an online multisource multisink queueing network control problem characterized with self-organizing network structure and self-organizing job routing. We decompose the self-organizing queueing network control problem into a series of interrelated Markov Decision Processes and construct a control decision model for them based on the coupled reinforcement learning (RL) architecture. To maximize the mean time averaged weighted throughput of the jobs through the network, we propose a reinforcement learning algorithm with time averaged reward to deal with the control decision model and obtain a control policy integrating the jobs routing selection strategy and the jobs sequencing strategy. Computational experiments verify the learning ability and the effectiveness of the proposed reinforcement learning algorithm applied in the investigated self-organizing network control problem.

scholarly journals Solving a Joint Pricing and Inventory Control Problem for Perishables via Deep Reinforcement Learning

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Rui Wang ◽  
Xianghua Gan ◽  
Qing Li ◽  
Xiao Yan
Keyword(s):  
Reinforcement Learning ◽  
Control Problem ◽  
Inventory Control ◽  
Learning Algorithm ◽  
Planning Horizon ◽  
Periodic Review ◽  
Current Period ◽  
Joint Pricing ◽  
Inventory Control Problem ◽  
Reinforcement Learning Algorithm

We study a joint pricing and inventory control problem for perishables with positive lead time in a finite horizon periodic-review system. Unlike most studies considering a continuous density function of demand, in our paper the customer demand depends on the price of current period and arrives according to a homogeneous Poisson process. We consider both backlogging and lost-sales cases, and our goal is to find a simultaneously ordering and pricing policy to maximize the expected discounted profit over the planning horizon. When there is no fixed ordering cost involved, we design a deep reinforcement learning algorithm to obtain a near-optimal ordering policy and show that there are some monotonicity properties in the learned policy. We also show that our deep reinforcement learning algorithm achieves a better performance than tabular-based Q-learning algorithms. When a fixed ordering cost is involved, we show that our deep reinforcement learning algorithm is effective and efficient, under which the problem of “curse of dimension” is circumvented.

Reinforcement Learning Algorithm for Load Balancing in Self‐Organizing Networks

Wiley 5G Ref ◽  
2020 ◽  
pp. 1-23
Author(s):  
Mohammed S. Ali ◽  
Pierre Coucheney ◽  
Marceau Coupechoux
Keyword(s):  
Reinforcement Learning ◽  
Load Balancing ◽  
Learning Algorithm ◽  
Self Organizing Networks ◽  
Self Organizing ◽  
Reinforcement Learning Algorithm

scholarly journals Computational Design of Modular Robots Based on Genetic Algorithm and Reinforcement Learning

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 471
Author(s):  
Jai Hoon Park ◽  
Kang Hoon Lee
Keyword(s):  
Genetic Algorithm ◽  
Reinforcement Learning ◽  
Design Space ◽  
Learning Algorithm ◽  
Computational Design ◽  
Computational Method ◽  
Learning Ability ◽  
Modular Robots ◽  
Control Mechanisms ◽  
Candidate Structure

Designing novel robots that can cope with a specific task is a challenging problem because of the enormous design space that involves both morphological structures and control mechanisms. To this end, we present a computational method for automating the design of modular robots. Our method employs a genetic algorithm to evolve robotic structures as an outer optimization, and it applies a reinforcement learning algorithm to each candidate structure to train its behavior and evaluate its potential learning ability as an inner optimization. The size of the design space is reduced significantly by evolving only the robotic structure and by performing behavioral optimization using a separate training algorithm compared to that when both the structure and behavior are evolved simultaneously. Mutual dependence between evolution and learning is achieved by regarding the mean cumulative rewards of a candidate structure in the reinforcement learning as its fitness in the genetic algorithm. Therefore, our method searches for prospective robotic structures that can potentially lead to near-optimal behaviors if trained sufficiently. We demonstrate the usefulness of our method through several effective design results that were automatically generated in the process of experimenting with actual modular robotics kit.

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