Continuous Reinforcement Learning Algorithm for Skills Learning in an Autonomous Mobile Robot

2005 ◽  
pp. 137-165
Author(s):  
Ma Jesús López Boada ◽  
Ramón Barber ◽  
Verónica Egido ◽  
Miguel Ángel Salichs
Keyword(s):  
Reinforcement Learning ◽  
Mobile Robot ◽  
Learning Algorithm ◽  
Autonomous Mobile Robot ◽  
Continuous Reinforcement ◽  
Skills Learning ◽  
Reinforcement Learning Algorithm

scholarly journals A Review of Mobile Robot Path Planning Based on Deep Reinforcement Learning Algorithm

2021 ◽  
Vol 2138 (1) ◽  
pp. 012011
Author(s):  
Yanwei Zhao ◽  
Yinong Zhang ◽  
Shuying Wang
Keyword(s):  
Deep Learning ◽  
Reinforcement Learning ◽  
Path Planning ◽  
Mobile Robot ◽  
Video Game ◽  
Autonomous Navigation ◽  
Learning Algorithm ◽  
Basic Knowledge ◽  
Target Point ◽  
Reinforcement Learning Algorithm

Abstract Path planning refers to that the mobile robot can obtain the surrounding environment information and its own state information through the sensor carried by itself, which can avoid obstacles and move towards the target point. Deep reinforcement learning consists of two parts: reinforcement learning and deep learning, mainly used to deal with perception and decision-making problems, has become an important research branch in the field of artificial intelligence. This paper first introduces the basic knowledge of deep learning and reinforcement learning. Then, the research status of deep reinforcement learning algorithm based on value function and strategy gradient in path planning is described, and the application research of deep reinforcement learning in computer game, video game and autonomous navigation is described. Finally, I made a brief summary and outlook on the algorithms and applications of deep reinforcement learning.

Genetic Q-Fuzzy Based Intelligent Control for Mobile Robot Navigation

2004 ◽  
Author(s):  
V. Ram Mohan Parimi ◽  
Devendra P. Garg
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Reinforcement Learning ◽  
Mobile Robot ◽  
Fuzzy Logic Controller ◽  
Learning Algorithm ◽  
Robot Navigation ◽  
Path Tracking ◽  
Mobile Robot Navigation ◽  
Reinforcement Learning Algorithm

This paper deals with the design and optimization of a Fuzzy Logic Controller that is used in the obstacle avoidance and path tracking problems of mobile robot navigation. The Fuzzy Logic controller is tuned using reinforcement learning controlled Genetic Algorithm. The operator probabilities of the Genetic Algorithm are adapted using reinforcement learning technique. The reinforcement learning algorithm used in this paper is Q-learning, a recently developed reinforcement learning algorithm. The performance of the Fuzzy-Logic Controller tuned with reinforcement controlled Genetic Algorithm is then compared with the one tuned with uncontrolled Genetic Algorithm. The theory is applied to a two-wheeled mobile robot’s path tracking problem. It is shown that the performance of the Fuzzy-Logic controller tuned by Genetic Algorithm controlled via reinforcement learning is better than the performance of the Fuzzy-Logic controller tuned via uncontrolled Genetic Algorithm.

Deep Deterministic Policy Gradient for Navigation of Mobile Robots

2021 ◽  
Vol 40 (1) ◽  
pp. 349-361
Author(s):  
Junior Costa de Jesus ◽  
Jair Augusto Bottega ◽  
Marco Antonio de Souza Leite Cuadros ◽  
Daniel Fernando Tello Gamarra
Keyword(s):  
Reinforcement Learning ◽  
Mobile Robot ◽  
Learning Algorithm ◽  
Mobile Robot Navigation ◽  
Simulated Environments ◽  
The Neural Network ◽  
Policy Gradient ◽  
Reward Functions ◽  
Continuous Actions ◽  
Reinforcement Learning Algorithm

This article describes the use of the Deep Deterministic Policy Gradient network, a deep reinforcement learning algorithm, for mobile robot navigation. The neural network structure has as inputs laser range findings, angular and linear velocities of the robot, and position and orientation of the mobile robot with respect to a goal position. The outputs of the network will be the angular and linear velocities used as control signals for the robot. The experiments demonstrated that deep reinforcement learning’s techniques that uses continuous actions, are efficient for decision-making in a mobile robot. Nevertheless, the design of the reward functions constitutes an important issue in the performance of deep reinforcement learning algorithms. In order to show the performance of the Deep Reinforcement Learning algorithm, we have applied successfully the proposed architecture in simulated environments and in experiments with a real robot.

scholarly journals Mobile Robot Path Optimization Technique Based on Reinforcement Learning Algorithm in Warehouse Environment

2021 ◽  
Vol 11 (3) ◽  
pp. 1209
Author(s):  
HyeokSoo Lee ◽  
Jongpil Jeong
Keyword(s):  
Reinforcement Learning ◽  
Mobile Robot ◽  
Learning Algorithm ◽  
Optimization Technique ◽  
Path Optimization ◽  
Learning Technology ◽  
Learning Techniques ◽  
Optimization Simulation ◽  
Robot Path ◽  
Reinforcement Learning Algorithm

This paper reports on the use of reinforcement learning technology for optimizing mobile robot paths in a warehouse environment with automated logistics. First, we compared the results of experiments conducted using two basic algorithms to identify the fundamentals required for planning the path of a mobile robot and utilizing reinforcement learning techniques for path optimization. The algorithms were tested using a path optimization simulation of a mobile robot in same experimental environment and conditions. Thereafter, we attempted to improve the previous experiment and conducted additional experiments to confirm the improvement. The experimental results helped us understand the characteristics and differences in the reinforcement learning algorithm. The findings of this study will facilitate our understanding of the basic concepts of reinforcement learning for further studies on more complex and realistic path optimization algorithm development.

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