While DRL-based algorithms offer a promising approach for optimizing resource allocation in complex systems like active STAR-RIS-aided communications, there are potential drawbacks and limitations to consider:
Complexity: DRL algorithms often require extensive computational resources due to their iterative nature and exploration-exploitation trade-offs. This complexity may limit their practical implementation in real-time scenarios or systems with stringent latency requirements.
Scalability: As the size of action spaces grows exponentially with increasing system parameters or variables to optimize, DRL algorithms may face challenges in handling large-scale optimization problems efficiently.
Convergence: Convergence guarantees are not always straightforward with DRL methods, especially when dealing with non-convex optimization problems like those encountered in resource allocation for wireless communication systems.
Training Data Requirements: Effective training of DRL models relies heavily on quality data samples that accurately represent the system dynamics. In dynamic environments or changing network conditions, maintaining relevant training data sets could be challenging.
Interpretability: The black-box nature of some deep reinforcement learning models may hinder interpretability and understanding of how decisions are made within complex resource allocation frameworks.