Integrate multi-agent simulation environment and multi-agent reinforcement learning (MARL) for real-world scenario

Author(s):  
Sangho Yeo ◽  
Seungjun Lee ◽  
Boreum Choi ◽  
Sangyoon Oh
2020 ◽  
Vol 10 (21) ◽  
pp. 7552
Author(s):  
Takumi Kato ◽  
Ryota Kamoshida

We propose a multi-agent simulation environment for logistics warehouses. Simulation is a crucial part of designing industrial systems, such as logistics warehouses. A warehouse is a multi-agent system (MAS) that consists of various autonomous subsystems with robots, material-handling equipment, and human workers. It is generally difficult to analyze the performance of a MAS thus, it is important to model a warehouse and conduct simulations to design and evaluate the possible system configurations. However, the cost of modeling warehouses and modifying the models is high because there are various components and interactions compared to conventional multi-agent simulations. We proposed a self-contained agent architecture and message architecture of a multi-agent simulation environment for logistics warehouses to reduce the simulation-model development and modification costs. We quantitatively evaluated our environment in terms of development costs by comparing such costs of our environment and a widely used multi-agent simulation environment.


2020 ◽  
Vol 08 (03) ◽  
pp. 253-260
Author(s):  
Jason Gibson ◽  
Tristan Schuler ◽  
Loy McGuire ◽  
Daniel M. Lofaro ◽  
Donald Sofge

This work develops and implements a multi-agent time-based path-planning method using A*. The purpose of this work is to create methods in which multi-agent systems can coordinate actions and complete them at the same time. We utilized A* with constraints defined by a dynamic model of each agent. The model for each agent is updated during each time step and the resulting control is determined. This results in a translational path that each of the agents is physically capable of completing in synchrony. The resulting path is given to the agents as a sequence of waypoints. Periodic updates of the path are calculated, utilizing real-world position and velocity information, as the agents complete the task to account for external disturbances. Our methodology is tested in a dynamic simulation environment as well as on real-world lighter-than-air robotic agents.


Author(s):  
Souvik Barat ◽  
Prashant Kumar ◽  
Monika Gajrani ◽  
Harshad Khadilkar ◽  
Hardik Meisheri ◽  
...  

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