scholarly journals Distributed multi-agent deep reinforcement learning for cooperative multi-robot pursuit

2020 ◽  
Author(s):  
Chao Yu ◽  
Yinzhao Dong ◽  
Yangning Li ◽  
Yatong Chen
Keyword(s):  
Reinforcement Learning ◽  
Multi Agent ◽  
Multi Robot

Multi-Agent Reinforcement Learning Based on K-Means Clustering in Multi-Robot Cooperative Systems

2011 ◽  
Vol 216 ◽  
pp. 75-80 ◽  
Author(s):  
Chang An Liu ◽  
Fei Liu ◽  
Chun Yang Liu ◽  
Hua Wu
Keyword(s):  
Reinforcement Learning ◽  
State Space ◽  
Experimental Results ◽  
Learning Ability ◽  
Learning Method ◽  
Q Learning ◽  
State Space Explosion ◽  
Multi Agent ◽  
Robot Cooperation ◽  
Multi Robot

To solve the curse of dimensionality problem in multi-agent reinforcement learning, a learning method based on k-means is presented in this paper. In this method, the environmental state is represented as key state factors. The state space explosion is avoided by classifying states into different clusters using k-means. The learning rate is improved by assigning different states to existent clusters, as well as corresponding strategy. Compared to traditional Q-learning, our experimental results of the multi-robot cooperation show that our scheme improves the team learning ability efficiently. Meanwhile, the cooperation efficiency can be enhanced successfully.

scholarly journals Fuzzy Reinforcement Learning and Curriculum Transfer Learning for Micromanagement in Multi-Robot Confrontation

Information ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 341 ◽  
Author(s):  
Hu ◽  
Xu
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Transfer Learning ◽  
Action Function ◽  
State Action ◽  
Markov Decision ◽  
Decision Making System ◽  
Multi Agent ◽  
Function Approximator ◽  
Multi Robot

Multi-Robot Confrontation on physics-based simulators is a complex and time-consuming task, but simulators are required to evaluate the performance of the advanced algorithms. Recently, a few advanced algorithms have been able to produce considerably complex levels in the context of the robot confrontation system when the agents are facing multiple opponents. Meanwhile, the current confrontation decision-making system suffers from difficulties in optimization and generalization. In this paper, a fuzzy reinforcement learning (RL) and the curriculum transfer learning are applied to the micromanagement for robot confrontation system. Firstly, an improved Qlearning in the semi-Markov decision-making process is designed to train the agent and an efficient RL model is defined to avoid the curse of dimensionality. Secondly, a multi-agent RL algorithm with parameter sharing is proposed to train the agents. We use a neural network with adaptive momentum acceleration as a function approximator to estimate the state-action function. Then, a method of fuzzy logic is used to regulate the learning rate of RL. Thirdly, a curriculum transfer learning method is used to extend the RL model to more difficult scenarios, which ensures the generalization of the decision-making system. The experimental results show that the proposed method is effective.

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.

Multi-agent deep reinforcement learning with type-based hierarchical group communication

2021 ◽  
Author(s):  
Hao Jiang ◽  
Dianxi Shi ◽  
Chao Xue ◽  
Yajie Wang ◽  
Gongju Wang ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Group Communication ◽  
Multi Agent ◽  
Hierarchical Group
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