Anti-conflict AGV path planning in automated container terminals based on multi-agent reinforcement learning

2021 ◽  
pp. 1-16
Author(s):  
Hongtao Hu ◽  
Xurui Yang ◽  
Shichang Xiao ◽  
Feiyang Wang
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Container Terminals ◽  
Multi Agent

scholarly journals Joint Optimization of Multi-UAV Target Assignment and Path Planning Based on Multi-Agent Reinforcement Learning

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 146264-146272 ◽  
Author(s):  
Han Qie ◽  
Dianxi Shi ◽  
Tianlong Shen ◽  
Xinhai Xu ◽  
Yuan Li ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Joint Optimization ◽  
Target Assignment ◽  
Multi Agent ◽  
Multi Uav

Research on Path-Planning of Manipulator Based on Multi-Agent Reinforcement Learning

2010 ◽  
Vol 44-47 ◽  
pp. 2116-2120
Author(s):  
Liang Tong
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Dynamic Characteristic ◽  
Simulation Experiment ◽  
Robot Manipulator ◽  
Variable Structure ◽  
Learning Method ◽  
New Approach ◽  
Multi Agent ◽  
Kohonen Net

Because of the dynamic characteristic of high nonlinear,strong coupling and variable structure,it is difficult to perform effective controlling on the robot manipulator by conventional controlling theory.In this paper,a new approach of multi-agent reinforcement learning method based on Kohonen net is proposed which is used in the multi-agent environment of robot manipulator path-planning and the simulation experiment shows the validity of this method.

scholarly journals Applications and Challenges of Deep Reinforcement Learning in Multi-robot Path Planning

2021 ◽  
Vol 5 (6) ◽  
pp. 25-29
Author(s):  
Tianyun Qiu ◽  
Yaxuan Cheng
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Research Area ◽  
Training Methods ◽  
Multi Agent Systems ◽  
Robot Path Planning ◽  
Practical Applications ◽  
Multi Agent ◽  
Robot Path ◽  
Multi Robot

With the rapid advancement of deep reinforcement learning (DRL) in multi-agent systems, a variety of practical application challenges and solutions in the direction of multi-agent deep reinforcement learning (MADRL) are surfacing. Path planning in a collision-free environment is essential for many robots to do tasks quickly and efficiently, and path planning for multiple robots using deep reinforcement learning is a new research area in the field of robotics and artificial intelligence. In this paper, we sort out the training methods for multi-robot path planning, as well as summarize the practical applications in the field of DRL-based multi-robot path planning based on the methods; finally, we suggest possible research directions for researchers.

scholarly journals Improved Multi-Agent Reinforcement Learning for Path Planning-Based Crowd Simulation

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 73841-73855 ◽  
Author(s):  
Qingqing Wang ◽  
Hong Liu ◽  
Kaizhou Gao ◽  
Le Zhang
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Crowd Simulation ◽  
Multi Agent
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