This work studies a spatially-coupled monotone ResNet architecture whose one-step operator is a non-negative local aggregation, designed to satisfy monotonicity and stability at the operator level, and obtains operator-level convergence to the viscosity solution via the Barles–Souganidis framework.
Deep reinforcement learning (DRL) relies on neural networks trained under temporally correlated experience, evolving replay distributions, and bootstrapped targets, conditions that can destabilize neural function approximation. This study investigates controllable orthogonalization as a network-level mechanism for impr...
Jia Yu, Dun Su, Hua Cui et al.· Informatics· 0 citations
This work takes the exploratory, reinforcement learning (RL) framework of Wang, Zariphopoulou, and Zhou with relaxed controls and entropy regularization with the objective to develop theoretically grounded, efficient and scalable RL algorithms to learn both the optimal value functions and optimal feedback control polic...
Jing-Sheng Ma, Gao-Zhan Wang, Jian-Feng Zhang et al.· 0 citations
Concurrent learning (CL) has recently been extended to Lyapunov-based deep neural networks (DNNs) to obtain parameter convergence under finite rather than persistent excitation. However, the existing first-order law converges slowly, especially for high-dimensional systems and overparameterized networks. To address thi...
A physics-informed RL (PIRL) framework that combines the complementary strengths of PINNs and RL for stochastic reach-avoid analysis, and develops a scheduled PIRL algorithm in which temporal-difference actor-critic learning first guides the critic toward a meaningful approximation of the reach-avoid value function.
This work provides a system-theoretic interpretation of generalization in learning-enabled dynamical systems arising in data-driven optimization and feedback control approximation, and establishes a matrix inequality-based certificate and a uniform stability bound that separates the one-sample sensitivity of the learne...
Obtaining the optimal action-value function in Markov decision processes is computationally intensive in large state--action spaces. In this study, we present statistically rigorous convergence results for a robust reinforcement learning algorithm warm-started by a transformer-based action-value function prediction, wh...
Suman Banerjee, Hiroyasu Tsukamoto· 0 citations
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