# Application of Reinforcement Learning in Decision Systems: Lift Control Case Study
## 6 Conclusions and Future Works  
In conclusion, the study successfully demonstrates a viable approach for utilizing reinforcement learning (RL) methods to address a complex real-world control problem, specifically lift control. The development of a universal lift emulator, enriched with real-world traffic distribution, modelled using data from ASTOR Company's intelligent building system, allowed for a comprehensive evaluation of RL-based strategies against conventional heuristic solutions. The research showcases that strategies devised by agents, trained using the advanced Deep Q-Learning algorithm, consistently outperform traditional heuristic solutions regarding average waiting time, served passengers, and overall reward.  
Furthermore, the exploration of three Experience Replay mechanisms, namely basic and Prioritized Replay Buffers and Memory Palace, demonstrated their effectiveness in the Deep Q-Learning algorithm. The learning process for each variant of DQL was smooth and stable, displaying rapid improvements in the average episode reward metric at the initial stages of training. While all agents, incorporating distinct Experience Replay mechanisms, formulated strategies surpassing traditional solutions, the agent trained with the Prioritized Replay Buffer mechanism achieved the highest average reward and the lowest average passenger waiting time in test simulations. Nevertheless, the discrepancies in metric values among the agents are marginal, making it challenging to declare the Prioritized Replay Buffer as the superior technique definitively.  
While this study provides valuable insights, there are several avenues for future research in this domain. Firstly, the exploration of different RL algorithms and architectures could yield further improvements in lift control optimization. Investigating the impact of varying state representations and reward functions on the learning process may enhance the adaptability of RL agents to diverse scenarios.  
Additionally, expanding the study to consider more complex lift systems, such as those with multiple cabins or buildings with interconnected elevators, could provide a more comprehensive understanding of reinforcement learning versatility. The integration of dynamic factors, such as varying passenger arrival rates depending on the system hour, could provide a more complex environment that can assess the adaptability of reinforcement learning methods.  
Conceptualization, T.S.; Methodology, M.W.; Software, M.W.; Formal analysis, M.W.; Data curation, M.W.; Writing--original draft, M.W.; Writing--review & editing, T.S.; Supervision, T.S. All authors have read and agreed to the published version of the manuscript.  
The research was supported by the AGH University of Krakow, KIS Department Research Grant No 16.16.120.7998: Informatics in theory and practice.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy constraints.  
The authors would like to thank the ASTOR sp. z o.o. company for providing access to their lift system in order to collect the data. Many thanks to the reviewers for their valuable remarks helping improve the paper.  
The authors declare no conflict of interest.