## 5 Conclusions  
In this paper, we reviewed the use of reinforcement learning as a path optimization technique in a warehouse environment. We built a simulation environment to test path navigation in a warehouse environment and tested the two most basic algorithms, the Q -Learning and Dyna-Q algorithms, for reinforcement learning. We then compared the experimental results in terms of the path search times and the final path lengths. Compared with the average of the results of the five experiments, the path search time of the Q- Learning  
Figure 15: Path results of Q-Learning with action based reward: (**a**) 1 st test; (**b**) 2 nd test; (**c**) 3 rd test; (**d**) 4 th test; (**e**) 5 th test.  
algorithm was about 4.1 times faster than the Dyna-Q algorithm's, and the final path length of Dyna-Q algorithm was about 37% shorter than the Q-Learning algorithm's. As a result, the experimental results of both Q-Learning and Dyna-Q algorithm showed unsatisfactory results for either the path search time or the final path length. To improve the results, we conducted various experiments using the Q-Learning algorithm and found that it does not perform well when finding search paths in a complex environment with many obstacles. The Q-Leaning algorithm has the property of finding a path at random. To minimize this random path searching trial, the target position was determined for movement, and the set reward value was high around the target position,' thus the performance and efficiency of the agent improved with 'dynamic reward adjustment based on action'. Agents mainly explore locations with high reward values. In further experiments, the path search time was about 5.5 times faster than the Dyna-Q algorithm's, and the final path length was about 71% shorter than the Q-Learning algorithm. It was confirmed that the path search time was slightly faster than the Q-Learning algorithm, and the final path length was improved to the same level as the Dyna-Q algorithm.  
It is a difficult goal to meet both minimization of path search time and high accuracy for path search. If the learning speed is too fast, it will not converge to the optimal value function, and if the learning speed is set too late, the learning may take too long. In this paper, we have experimentally confirmed how to reduce inefficient exploration with simple technique to improve path search accuracy and maintain path search time.  
In this study, we have seen how to use reinforcement learning for a single agent and a path optimization technique for a single path, but the warehouse environment in the field is much more complex and dynamically changing. In the automation warehouse, multiple mobile robots work simultaneously. Therefore, this basic study is an example of improving the basic algorithm in a single path condition for a single agent, and it is insufficient to apply the algorithm to an actual warehouse environment. Therefore, based on the obtained results, an in-depth study of the improved reinforcement learning algorithm is needed, considering highly challenging multi-agent environments to solve more complex and realistic problems.  
Conceptualization, H.L. and J.J.; methodology, H.L.; software, H.L.; validation, H.L. and J.J.; formal analysis, H.L.; investigation, H.L.; resources, J.J.; data curation, H.L.; writing-original draft preparation, H.L.; writing-review and editing, J.J.; visualization, H.L.; supervision, J.J.; project administration, J.J.; funding acquisition, J.J. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2020-2018-0-01417) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation).  
This work was supported by the Smart Factory Technological R&D Program S2727115 funded by Ministry of SMEs and Startups (MSS, Korea).  
The authors declare no conflict of interest.