Q-Learning Decision-Making Model for Robotic System

The proposed paper analyses the modern approaches to decision-making, based on reinforcement learning with practical implementation of Q-learning for tasks of robotics. Theoretical aspects of reinforcement learning are considered in comparison to other decision-making technologies. From practical po...

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Main Authors: Nevliudov, Igor, Tsymbal, Oleksandr, Bronnikov, Artem
Format: Conference Proceeding
Language:English
Subjects:
Computational modeling
Decision making
Mathematical models
Numerical models
Q-learning
Reinforcement learning
Robot sensing systems
Robotics
Service robots
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creator Nevliudov, Igor
Tsymbal, Oleksandr
Bronnikov, Artem
description The proposed paper analyses the modern approaches to decision-making, based on reinforcement learning with practical implementation of Q-learning for tasks of robotics. Theoretical aspects of reinforcement learning are considered in comparison to other decision-making technologies. From practical point, Q-learning is proposed to use for robot's actions observation, for reduction of decision-making process duration and as a decision-making model for mobile robot movements inside complex workspace. Materials of paper include the results of numerical experiments, which simulate workspaces of robot with different levels of complexity for paths with various starting points.
doi_str_mv 10.1109/EWDTS59469.2023.10297075
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subjects Computational modeling
Decision making
Mathematical models
Numerical models
Q-learning
Reinforcement learning
Robot sensing systems
Robotics
Service robots
title Q-Learning Decision-Making Model for Robotic System
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