# Decision-Time Learning and Planning Integrated Control for the Mild Hyperbaric Chamber
## 5 Performance Evaluation
### Model Learning Evaluation
The proposed SCR learning approach further enhances the model's understanding of complex environmental dynamics by extracting intrinsic information and achieves further improvements over LSTM. Figure 6 illustrates the asymptotic and convergence performance of different environment model architectures. The experimental results indicate that the LSTM and SCR significantly outperform the MLP-based approach in terms of data efficiency and stability. In contrast, the MLP-based approach exhibits a relatively slower convergence rate during training and shows greater fluctuations, suggesting certain limitations in learning complex dynamic environments. Next, we evaluate the effectiveness of model output optimization from two perspectives: average score and performance profile. For performance profile, we analyze the distribution of scores, which provides insights into the distribution of the algorithm's performance. We compare four algorithms: (1) RS (Random Shooting): the random shooting search method; (2) CEM (Cross-Entropy Method): standard cross-entropy-based approach; (3) PETS (Probabilistic Ensemble and Trajectory Sampling): CEM method that integrates PETS; amd (4) CEM-SEP (Cross-Entropy Method with separated uncertainty measurement): our proposed method, which separately quantifies epistemic uncertainty and aleatoric uncertainty. The control system operates based on the designed decision-time planning with model predictive control. Figure 5: SCR outperforms other methods in average score with 95% CI of model prediction. Figure 7 compares the performance of the algorithms in model output optimization. In planning score metrics, CEM outperforms RS by approximately 6.1%. RS relies on uniform random sampling across the entire action space, which struggles to identify optimal policies in complex dynamic environments. In contrast, CEM utilizes an adaptive probability distribution update mechanism to iteratively converge toward high-quality actions, thereby improving decision efficiency. With the introduction of probabilistic ensemble and trajectory sampling, control precision further improves by approximately 4.3%. PETS enhances model prediction accuracy by training multiple environment models and estimating uncertainty during trajectory sampling. By separately modeling aleatoric and epistemic uncertainty, and then combining both for joint modeling, the CEM-SEP algorithm achieves a 1.2% performance increase compared to the PETS algorithm. Figure 8 compares the performance distribution of different output optimization methods as the fractions of scores that are greater than its normalized average score. It can be observed that the CEM method, by adaptively adjusting the sampling distribution, more effectively converges to high-quality action sequences compared to the RS method. Building upon the CEM method, further optimization of model output uncertainty results in PETS and CEM-SEP achieving more desirable output distributions. Figure 6: Asymptotic and convergence performance of model prediction. Figure 7: CEM-SEP outperforms other methods in average score with 95% CI of different model outputs.