The Bio-Insect and Artificial Robots Interaction Based on Multi-Agent Reinforcement Learning

2009 ◽  
Author(s):  
Young-Cheol Choi ◽  
Hyo-Sung Ahn
Keyword(s):  
Reinforcement Learning ◽  
Target Position ◽  
Research Topic ◽  
Learning System ◽  
Learning Theories ◽  
Robot Interaction ◽  
Artificial Intelligent ◽  
Intelligent Robots ◽  
Challenging Research ◽  
Multi Agent

Multi-agent reinforcement learning is a challenging research topic used in various fields including robotics, artificial intelligence, distributed control, and so on. Recently, there have been lots of efforts to develop multi-agent reinforcement learning theories; but there exist many difficulties in multi-agent reinforcement learning system. In this paper, we introduce our on-going project BRIDS (Bio-insect and artificial Robot Interaction based on Distributed System), which is for interactions between a bio-insect and artificial robots using multi-agent reinforcement learning. The main objective of this project is to drive the bio-insect to the desired position using a group of artificial intelligent robots. Simulation results show that artificial intelligent robots drive the bio-insect to the target position using reinforcement learning.

scholarly journals Design of an Adaptive e-Learning System based on Multi-Agent Approach and Reinforcement Learning

2021 ◽  
Vol 11 (1) ◽  
pp. 6637-6644
Author(s):  
H. El Fazazi ◽  
M. Elgarej ◽  
M. Qbadou ◽  
K. Mansouri
Keyword(s):  
Reinforcement Learning ◽  
Learning Style ◽  
Learning Process ◽  
Learning Experience ◽  
Personalized Learning ◽  
Learning Objects ◽  
Learning Systems ◽  
Learning System ◽  
E Learning ◽  
Multi Agent

Adaptive e-learning systems are created to facilitate the learning process. These systems are able to suggest the student the most suitable pedagogical strategy and to extract the information and characteristics of the learners. A multi-agent system is a collection of organized and independent agents that communicate with each other to resolve a problem or complete a well-defined objective. These agents are always in communication and they can be homogeneous or heterogeneous and may or may not have common objectives. The application of the multi-agent approach in adaptive e-learning systems can enhance the learning process quality by customizing the contents to students’ needs. The agents in these systems collaborate to provide a personalized learning experience. In this paper, a design of an adaptative e-learning system based on a multi-agent approach and reinforcement learning is presented. The main objective of this system is the recommendation to the students of a learning path that meets their characteristics and preferences using the Q-learning algorithm. The proposed system is focused on three principal characteristics, the learning style according to the Felder-Silverman learning style model, the knowledge level, and the student's possible disabilities. Three types of disabilities were taken into account, namely hearing impairments, visual impairments, and dyslexia. The system will be able to provide the students with a sequence of learning objects that matches their profiles for a personalized learning experience.

scholarly journals Robotic Obstacle Avoidance: A Virtual Modeling And Reinforcement Learning

2021 ◽  
Author(s):  
Ming-Fei Chen ◽  
Han-Hsien Tsai ◽  
Wen-Tse Hsiao
Keyword(s):  
Reinforcement Learning ◽  
Route Planning ◽  
Target Position ◽  
Learning System ◽  
Robotic Arm ◽  
Policy Gradient ◽  
Visualization Toolkit ◽  
Reward Functions ◽  
Virtual Modeling ◽  
Self Learning

Abstract This study developed a robotic arm self-learning system based on virtual modeling and reinforcement learning. Using the model of a robotic arm, information concerning obstacles in the environment, initial coordinates of the robotic arm, and the target position, this system automatically generated a set of rotational angles to enable a robotic arm to be positioned such that it can avoid all obstacles and reach a target. The developed program was divided into three parts. The first part involves robotic arm simulation and collision detection; specifically, images of a six-axis robotic arm and obstacles were input to the Visualization ToolKit library to visualize the movements and surrounding environment of the robotic arm. Subsequently, an oriented bounding box algorithm was used to determine whether collisions had occurred. The second part concerned machine-learning–based route planning. The TensorFlow was used to establish a deep deterministic policy gradient model, and reinforcement learning was employed for the response to environmental variables. Different reward functions were designed for tests and discussions, and the program’s practicality was verified through actual machine operations. Finally, the application of reinforcement learning in route planning for a robotic arm was proved feasible by the experiment. Such an application facilitated automatic route planning and achieved an error of less than 10 mm from the target position.

Modified Multi-Agent Reinforcement Learning System to Find Ships' Courses

2014 ◽  
Vol 2 ◽  
pp. 487-490
Author(s):  
Tomohiro Tanigawa ◽  
Takeshi Kamio ◽  
Kunihiko Mitsubori ◽  
Takahiro Tanaka ◽  
Hisato Fujisaka ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Learning System ◽  
Multi Agent

scholarly journals Research on decision-making strategy of soccer robot based on multi-agent reinforcement learning

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142091696
Author(s):  
Xiaoli Liu
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Division Of Labour ◽  
Learning System ◽  
Selection Strategy ◽  
Multi Agent System ◽  
Soccer Robot ◽  
Agent System ◽  
Multi Agent

This article studies a multi-agent reinforcement learning algorithm based on agent action prediction. In multi-agent system, the action of learning agent selection is inevitably affected by the action of other agents, so the reinforcement learning system needs to consider the joint state and joint action of multi-agent based on this. In addition, the application of this method in the cooperative strategy learning of soccer robot is studied, so that the multi-agent system can pass through the environment. To realize the division of labour and cooperation of multi-robots, the interactive learning is used to master the behaviour strategy. Combined with the characteristics of decision-making of soccer robot, this article analyses the role transformation and experience sharing of multi-agent reinforcement learning, and applies it to the local attack strategy of soccer robot, uses this algorithm to learn the action selection strategy of the main robot in the team, and uses Matlab platform for simulation verification. The experimental results prove the effectiveness of the research method, and the superiority of the proposed method is validated compared with some simple methods.

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