While deep reinforcement learning (DRL) offers significant advantages for disturbance rejection in nonlinear systems, it also comes with certain limitations and drawbacks:
Sample Efficiency: DRL algorithms often require a large number of samples to learn an effective control policy, which can be time-consuming and computationally expensive. To address this limitation, techniques like experience replay or prioritized experience replay can be employed to improve sample efficiency and accelerate learning.
Exploration-Exploitation Trade-off: Balancing exploration (trying new actions) and exploitation (leveraging known actions) is crucial in DRL. In complex systems, this trade-off can be challenging, leading to suboptimal control policies. Advanced exploration strategies, such as epsilon-greedy policies or noisy networks, can help address this issue.
Generalization to Unseen Scenarios: DRL models may struggle to generalize well to unseen scenarios or environments, especially in aerospace applications where conditions can vary widely. Incorporating transfer learning techniques or domain randomization during training can enhance the model's ability to adapt to new situations.
Safety and Stability: Ensuring the safety and stability of DRL-controlled systems is paramount, as incorrect actions can have severe consequences in aerospace settings. Implementing safety constraints, reward shaping, or using model-based reinforcement learning approaches can mitigate risks and improve system stability.
Interpretability and Explainability: DRL models are often considered black boxes, making it challenging to interpret their decisions or understand the underlying control mechanisms. Employing techniques like attention mechanisms, saliency maps, or model introspection can enhance the interpretability of DRL algorithms.
By addressing these limitations through advanced algorithmic enhancements, safety measures, and interpretability techniques, the drawbacks of using DRL for disturbance rejection can be mitigated, making it a more robust and reliable control technique for complex systems.