A study of reinforcement learning with knowledge sharing -Applications to real mobile robots-

2005 ◽  
Author(s):  
K. Ito ◽  
Y. Imoto ◽  
H. Taguchi ◽  
A. Gofuku
Keyword(s):  
Reinforcement Learning ◽  
Mobile Robots ◽  
Knowledge Sharing

scholarly journals An Improved Reinforcement Learning Algorithm for Cooperative Behaviors of Mobile Robots

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yong Song ◽  
Yibin Li ◽  
Xiaoli Wang ◽  
Xin Ma ◽  
Jiuhong Ruan
Keyword(s):  
Reinforcement Learning ◽  
Mobile Robots ◽  
Knowledge Sharing ◽  
State Space ◽  
Learning Algorithm ◽  
The State ◽  
Convergence Speed ◽  
Exponential Increase ◽  
Cooperative Behaviors ◽  
Reinforcement Learning Algorithm

Reinforcement learning algorithm for multirobot will become very slow when the number of robots is increasing resulting in an exponential increase of state space. A sequentialQ-learning based on knowledge sharing is presented. The rule repository of robots behaviors is firstly initialized in the process of reinforcement learning. Mobile robots obtain present environmental state by sensors. Then the state will be matched to determine if the relevant behavior rule has been stored in the database. If the rule is present, an action will be chosen in accordance with the knowledge and the rules, and the matching weight will be refined. Otherwise the new rule will be appended to the database. The robots learn according to a given sequence and share the behavior database. We examine the algorithm by multirobot following-surrounding behavior, and find that the improved algorithm can effectively accelerate the convergence speed.

The Knowledge Sharing Based Reinforcement Learning Algorithm for Collective Behaviors of Mobile Robots

2012 ◽  
Vol 588-589 ◽  
pp. 1515-1518
Author(s):  
Yong Song ◽  
Bing Liu ◽  
Yi Bin Li
Keyword(s):  
Reinforcement Learning ◽  
Mobile Robots ◽  
Knowledge Sharing ◽  
State Space ◽  
Learning Algorithm ◽  
Collective Behaviors ◽  
Q Learning ◽  
Exponential Increase ◽  
Multi Robot ◽  
Reinforcement Learning Algorithm

Reinforcement learning algorithm for multi-robot may will become very slow when the number of robots is increasing resulting in an exponential increase of state space. A sequential Q-learning base on knowledge sharing is presented. The rule repository of robots behaviors is firstly initialized in the process of reinforcement learning. Mobile robots obtain present environmental state by sensors. Then the state will be matched to determine if the relevant behavior rule has been stored in database. If the rule is present, an action will be chosen in accordance with the knowledge and the rules, and the matching weight will be refined. Otherwise the new rule will be joined in the database. The robots learn according to a given sequence and share the behavior database. We examine the algorithm by multi-robot following-surrounding behavior, and find that the improved algorithm can effectively accelerate the convergence speed.

State-chain sequential feedback reinforcement learning for path planning of autonomous mobile robots

2013 ◽  
Vol 14 (3) ◽  
pp. 167-178 ◽  
Author(s):  
Xin Ma ◽  
Ya Xu ◽  
Guo-qiang Sun ◽  
Li-xia Deng ◽  
Yi-bin Li
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Mobile Robots ◽  
Autonomous Mobile Robots

Decentralized Reinforcement Learning Applied to Mobile Robots

2017 ◽  
pp. 368-379 ◽  
Author(s):  
David L. Leottau ◽  
Aashish Vatsyayan ◽  
Javier Ruiz-del-Solar ◽  
Robert Babuška
Keyword(s):  
Reinforcement Learning ◽  
Mobile Robots

scholarly journals A novel mobile robot navigation method based on deep reinforcement learning

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142092167
Author(s):  
Hao Quan ◽  
Yansheng Li ◽  
Yi Zhang
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Mobile Robots ◽  
Recurrent Neural Network ◽  
Three Dimensional ◽  
Mobile Robot Navigation ◽  
Simulation Environment ◽  
Network Modules ◽  
Good Improvement ◽  
Dimensional Simulation

At present, the application of mobile robots is more and more extensive, and the movement of mobile robots cannot be separated from effective navigation, especially path exploration. Aiming at navigation problems, this article proposes a method based on deep reinforcement learning and recurrent neural network, which combines double net and recurrent neural network modules with reinforcement learning ideas. At the same time, this article designed the corresponding parameter function to improve the performance of the model. In order to test the effectiveness of this method, based on the grid map model, this paper trains in a two-dimensional simulation environment, a three-dimensional TurtleBot simulation environment, and a physical robot environment, and obtains relevant data for peer-to-peer analysis. The experimental results show that the proposed algorithm has a good improvement in path finding efficiency and path length.

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