## 7 Conclusions and Future Works  
The implemented GA successfully optimised specific hyperparameters through various combinations of selection, crossover, and mutation methods. It demonstrated strong performance in fine-tuning the hyperparameters of the reinforcement learning-based deep learning algorithm. The best-performing genetic algorithm improved the fitness function value from 68.26 (initial best chromosome) to 979.16 after 200 iterations, highlighting the effectiveness of the proposed approach. In the context of wide ranges of hyperparameter values, such as those used in this study, balancing exploration and exploitation in the search space is essential. Higher levels of exploration allow for a more thorough examination of available values and the identification of optimal configurations, which translates into improved agent performance. Compared to lower-exploration methods, such as one-point crossover, higher-exploration methods, such as multi-point and arithmetic crossover, are more effective because they enable the discovery of a more diverse set of solutions. One-point crossover, while simpler, often leads to searching a limited range of configurations, resulting in lower agent performance as it restricts the discovery of valuable solutions outside of local optima. Methods like arithmetic crossover, especially when combined with moderate mutation, allow for a better balance between exploration and exploitation. The additional diversity introduced by more advanced crossover and mutation techniques ensures that the genetic  
Figure 15: Hyperparameters vs. agent’s score by loss probability. (**a**) Learning rate vs. score by loss probability. (**b**) Target update vs. score by loss probability. (**c**) Replay buffer size vs. score by loss probability. (**d**) Gamma vs. score by loss probability. algorithm not only explores a wider range of the hyperparameter space but also adapts more effectively to varying values, increasing the likelihood of achieving higher results. In particular, the combination of arithmetic and multi-point crossover with boundary mutation enables a very high level of exploration without losing promising individuals. In the subject of the impact of the values of selected hyperparameters on the operation of the DQN algorithm, the relationship between the hyperparameter value and the agent result obtained was visible. Despite the fact that in no case was there a significant correlation between the hyperparameters themselves and the hyperparameters and the result, the complexity of the problem could be observed because specifying one hyperparameter value did not guarantee obtaining a high result. This suggests that there are hidden dependencies between the values of the given hyperparameters. In the optimised DQN algorithm, a decrease in the learning rate, more frequent updates of the agent's neural network, and larger gamma values allowed the DQN agent to obtain higher results. The size of the repetition buffer in this case had an impact on the obtained result but was not as pronounced as in the case of the hyperparameters mentioned above. Additionally, the extended cross-seeded analysis confirmed that moderate learning rates (e.g., 0.01) deliver significantly higher performance in the final episodes, emphasising the crucial role of learning rate selection for stable DQN convergence. The mini case study on the CartPole environment with simulated sensor dropouts demonstrated the challenges of maintaining optimisation performance under partial observability. The best-performing GA configuration was evaluated under varying levels of sensor dropout (0%, 5%, and 20%). The results revealed a significant decline in optimisation efficiency as the probability of missing observations increased. In the fully observable scenario (0% dropout), the GA achieved a peak fitness value above 900, indicating effective optimisation. However, with 5% dropout, performance stabilised at around 200, and with 20% dropout, the GA struggled to improve, achieving only minimal gains.