Robustness and Generalization in Quantum Reinforcement Learning via Lipschitz Regularization
This work addresses robustness and generalization problems in quantum reinforcement learning, offering an incremental improvement with practical benefits for the field.
The paper tackles robustness and generalization issues in quantum reinforcement learning by proposing RegQPG, a regularized quantum policy gradient algorithm using Lipschitz bounds, which improves policy performance and reduces training failures, as validated through numerical experiments.
Quantum machine learning leverages quantum computing to enhance accuracy and reduce model complexity compared to classical approaches, promising significant advancements in various fields. Within this domain, quantum reinforcement learning has garnered attention, often realized using variational quantum circuits to approximate the policy function. This paper addresses the robustness and generalization of quantum reinforcement learning by combining principles from quantum computing and control theory. Leveraging recent results on robust quantum machine learning, we utilize Lipschitz bounds to propose a regularized version of a quantum policy gradient approach, named the RegQPG algorithm. We show that training with RegQPG improves the robustness and generalization of the resulting policies. Furthermore, we introduce an algorithmic variant that incorporates curriculum learning, which minimizes failures during training. Our findings are validated through numerical experiments, demonstrating the practical benefits of our approach.