A Generic Software Framework for Carsharing Modelling Based on a Large-Scale Multi-agent Traffic Simulation Platform

2017 ◽  
pp. 88-111 ◽  
Author(s):  
Mohamed Haitam Laarabi ◽  
Raffaele Bruno
Keyword(s):  
Large Scale ◽  
Traffic Simulation ◽  
Software Framework ◽  
Simulation Platform ◽  
Multi Agent

HGHA: task allocation and path planning for warehouse agents

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yandong Liu ◽  
Dong Han ◽  
Lujia Wang ◽  
Cheng-Zhong Xu
Keyword(s):  
Path Planning ◽  
Task Allocation ◽  
Large Scale ◽  
Rapid Development ◽  
Hierarchical Architecture ◽  
Simulation Platform ◽  
Content Type ◽  
Agent Simulation ◽  
Multi Agent ◽  
Logistics Demand

Purpose With the rapid development of e-commerce, logistics demand is increasing day by day. The modern warehousing with a multi-agent system as the core comes into being. This paper aims to study the task allocation and path-planning (TAPP) problem as required by the multi-agent warehouse system. Design/methodology/approach The TAPP problem targets to minimize the makespan by allocating tasks to the agents and planning collision-free paths for the agents. This paper presents the Hierarchical Genetic Highways Algorithm (HGHA), a hierarchical algorithm combining optimization and multi-agent path-finding (MAPF). The top-level is the genetic algorithm (GA), allocating tasks to agents in an optimized way. The lower level is the so-called highways local repair (HLR) process, avoiding the collisions by local repairment if and only if conflicts arise. Findings Experiments demonstrate that HGHA performs faster and more efficient for the warehouse scenario than max multi-flow. This paper also applies HGHA to TAPP instances with a hundred agents and a thousand storage locations in a customized warehouse simulation platform with MultiBots. Originality/value This paper formulates the multi-agent warehousing distribution problem, TAPP. The HGHA based on hierarchical architecture solves the TAPP accurately and quickly. Verifying the HGHA by the large-scale multi-agent simulation platform MultiBots.

scholarly journals Performance Analysis and Design Considerations of the Shallow Underwater Optical Wireless Communication System with Solar Noises Utilizing a Photon Tracing-Based Simulation Platform

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 632
Author(s):  
Xiaozheng Wang ◽  
Minglun Zhang ◽  
Hongyu Zhou ◽  
Xiaomin Ren
Keyword(s):  
Performance Analysis ◽  
Wireless Communication ◽  
Ambient Noise ◽  
Large Scale ◽  
Delay Spread ◽  
Simulation Platform ◽  
Optical Wireless Communication ◽  
Optical Wireless ◽  
Analysis And Design ◽  
The Impact

The performance of the underwater optical wireless communication (UOWC) system is highly affected by seawater´s inherent optical properties and the solar radiation from sunlight, especially for a shallow environment. The multipath effect and degradations in signal-to-noise ratio (SNR) due to absorption, scattering, and ambient noises can significantly limit the viable communication range, which poses key challenges to its large-scale commercial applications. To this end, this paper proposes a unified model for underwater channel characterization and system performance analysis in the presence of solar noises utilizing a photon tracing algorithm. Besides, we developed a generic simulation platform with configurable parameters and self-defined scenarios via MATLAB. Based on this platform, a comprehensive investigation of underwater channel impairments was conducted including temporal and spatial dispersion, illumination distribution pattern, and statistical attenuation with various oceanic types. The impact of ambient noise at different operation depths on the bit error rate (BER) performance of the shallow UOWC system was evaluated under typical specifications. Simulation results revealed that the multipath dispersion is tied closely to the multiple scattering phenomenon. The delay spread and ambient noise effect can be mitigated by considering a narrow field of view (FOV) and it also enables the system to exhibit optimal performance on combining with a wide aperture.

scholarly journals A Confrontation Decision-Making Method with Deep Reinforcement Learning and Knowledge Transfer for Multi-Agent System

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 631
Author(s):  
Chunyang Hu
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Knowledge Transfer ◽  
Large Scale ◽  
Effective Control ◽  
Small Scale ◽  
Learning Agent ◽  
Multi Agent ◽  
Transfer Method ◽  
Parameter Sharing

In this paper, deep reinforcement learning (DRL) and knowledge transfer are used to achieve the effective control of the learning agent for the confrontation in the multi-agent systems. Firstly, a multi-agent Deep Deterministic Policy Gradient (DDPG) algorithm with parameter sharing is proposed to achieve confrontation decision-making of multi-agent. In the process of training, the information of other agents is introduced to the critic network to improve the strategy of confrontation. The parameter sharing mechanism can reduce the loss of experience storage. In the DDPG algorithm, we use four neural networks to generate real-time action and Q-value function respectively and use a momentum mechanism to optimize the training process to accelerate the convergence rate for the neural network. Secondly, this paper introduces an auxiliary controller using a policy-based reinforcement learning (RL) method to achieve the assistant decision-making for the game agent. In addition, an effective reward function is used to help agents balance losses of enemies and our side. Furthermore, this paper also uses the knowledge transfer method to extend the learning model to more complex scenes and improve the generalization of the proposed confrontation model. Two confrontation decision-making experiments are designed to verify the effectiveness of the proposed method. In a small-scale task scenario, the trained agent can successfully learn to fight with the competitors and achieve a good winning rate. For large-scale confrontation scenarios, the knowledge transfer method can gradually improve the decision-making level of the learning agent.

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