Sunday 06 April 2025
Deep Reinforcement Learning is a key technology driving progress across various scientific and engineering fields, including wireless communication. However, its limited interpretability and generalizability remain major challenges. In supervised learning, generalizability is commonly evaluated through the generalization error using information-theoretic methods. In Deep Reinforcement Learning (DRL), the training data is sequential and not independent and identically distributed (i.i.d.), rendering traditional information-theoretic methods unsuitable for generalizability analysis.
To address this challenge, researchers have developed a novel analytical method to evaluate the generalizability of DRL algorithms. This approach involves modeling the evolution of states and actions in trained DRL algorithms as unknown discrete, stochastic, and nonlinear dynamical functions. The Koopman operator is then employed to approximate these functions, providing two interpretable representations for the evolution associated with states and actions.
The H∞norm is used to analyze the spectral features of the approximated Koopman operator, allowing researchers to evaluate the maximum impact of domain changes on the trained DRL performance. This approach has been applied to wireless communication scenarios, where it has been shown that the SAC algorithm exhibits higher reward values compared to PPO during training.
However, when faced with domain changes, such as variations in noise power or medium absorption factor, the PPO algorithm’s average reward is significantly more impacted than SAC’s. This highlights the importance of generalizability analysis for DRL algorithms, particularly in applications where domain changes are likely to occur.
The proposed analytical method offers valuable insights into the generalizability of DRL algorithms and provides a framework for evaluating their performance under different conditions. By analyzing the spectral features of the Koopman operator using the H∞norm, researchers can better understand how domain changes affect the trained algorithm’s behavior.
Furthermore, this approach can be extended to other domains where DRL is applied, such as robotics or finance. The method’s versatility and ability to provide interpretable results make it a valuable tool for researchers seeking to improve the performance of their DRL algorithms.
The use of Koopman operator theory and dynamic mode decomposition (DMD) has enabled researchers to analyze the evolution of states and actions in trained DRL algorithms, providing insights into the generalizability of these algorithms. The H∞norm has proven effective in evaluating the maximum impact of domain changes on the trained algorithm’s performance.
Cite this article: “Unlocking the Secrets of Deep Reinforcement Learning: A Generalizability Analysis for Wireless Communications”, The Science Archive, 2025.
Deep Reinforcement Learning, Koopman Operator, Generalizability, Wireless Communication, Information-Theoretic Methods, Sac Algorithm, Ppo Algorithm, Domain Changes, H∞Norm, Dynamic Mode Decomposition
