A Hierarchical Reinforcement Learning Based Approach for Multi-robot Cooperation in Unknown Environments

2012 ◽  
pp. 69-74 ◽  
Author(s):  
Yifan Cai ◽  
Simon X. Yang ◽  
Xin Xu ◽  
Gauri S. Mittal
Keyword(s):  
Reinforcement Learning ◽  
Hierarchical Reinforcement Learning ◽  
Unknown Environments ◽  
Robot Cooperation ◽  
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.

A PSO-based multi-robot cooperation method for target searching in unknown environments

2016 ◽  
Vol 177 ◽  
pp. 62-74 ◽  
Author(s):  
Masoud Dadgar ◽  
Shahram Jafari ◽  
Ali Hamzeh
Keyword(s):  
Unknown Environments ◽  
Robot Cooperation ◽  
Target Searching ◽  
Multi Robot

A GWO-based multi-robot cooperation method for target searching in unknown environments

2021 ◽  
pp. 115795
Author(s):  
Hongwei Tang ◽  
Wei Sun ◽  
Anping Lin ◽  
Min Xue ◽  
Xing Zhang
Keyword(s):  
Unknown Environments ◽  
Robot Cooperation ◽  
Target Searching ◽  
Multi Robot

A PSO-Based Approach with Fuzzy Obstacle Avoidance for Cooperative Multi-Robots in Unknown Environments

2016 ◽  
Vol 15 (01) ◽  
pp. 1650001 ◽  
Author(s):  
Yifan Cai ◽  
Simon X. Yang
Keyword(s):  
Obstacle Avoidance ◽  
Fitness Function ◽  
Field Function ◽  
Robot Trajectory ◽  
Unknown Environments ◽  
Cooperation Strategy ◽  
Key Issues ◽  
Robot Cooperation ◽  
Target Searching ◽  
Multi Robot

Cooperative exploration in unknown environments is fundamentally important in robotics, where the real-time path planning and proper task allocation strategies are the key issues for multi-robot cooperation. In this paper, a PSO-based approach, combined with a fuzzy obstacle avoidance module, is proposed for cooperative robots to accomplish target searching and foraging tasks in unknown environments. The proposed cooperation strategy for a multi-robot system makes use of the potential field function as the fitness function of PSO, while the proposed fuzzy obstacle-avoidance module improves the smoothness of robot trajectory. In the simulation studies, several scenarios with and without the fuzzy module are investigated. The robot trajectory smoothness improvement is demonstrated through the comparative studies.

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