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In this paper, research on the applications of artificial intelligence in implementing Deep Deterministic Policy Gradient (DDPG) on Gazebo model and the reality of mobile robot has been studied and applied. The goal of the experimental studies is to navigate the mobile robot to learn the best possible action to move in real-world environments when facing fixed and mobile obstacles. When the robot moves in an environment with obstacles, the robot will automatically control to avoid these obstacles. Then, the more time that can be maintained within a specific limit, the more rewards are accumulated and therefore better results will be achieved. The authors performed various tests with many transform parameters and proved that the DDPG algorithm is more efficient than algorithms like Q-learning, Machine learning, deep Q-network, etc. Then execute SLAM to recognize the robot positions, and virtual maps are precisely built and displayed in Rviz. The research results will be the basis for the design and construction of control algorithms for mobile robots and industrial robots applied in programming techniques and industrial factory automation control.

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