The paper introduces reinforcement learning for Continuous-Time Jump Markov Decision Processes (CTJMDPs) with general discrete state spaces and continuous/discrete actions. It develops entropy‑regularized continuous‑time control and establishes theoretical foundations for q‑learning in this setting, providing model‑free algorithms that outperform naive discretization. Numerical tests on network dynamic pricing demonstrate the method’s ability to learn near‑optimal policies and scale to large networks.
By Huiling Meng, Ningyuan Chen, Xuefeng Gao
arXiv:2606. 28669v1 Announce Type: new Abstract: To address parameter misspecification and sudden structural environmental changes in conventional stochastic differential game (SDG) frameworks, this paper introduces a distributional control approach that characterizes optimal strategies as probability distributions over actions, conditioned on the continuous state, the discrete regime state, and parameters.
By Congde Hu, Zhuo Jin, Danping Li, Lin Xu
The paper introduces a continuous‑time policy gradient framework that works with discrete observation data. It derives performance difference lemmas and policy gradient formulas for randomized and parametrized policies, proving exponential convergence for an entropy‑regularized policy flow and providing explicit convergence bounds for discrete updates. By integrating with PhiBE, the authors create model‑free actor‑critic algorithms that maintain the continuous‑time structure, and demonstrate their effectiveness on both linear‑quadratic and nonlinear robotic control tasks.
By Martin Hernandez, Xinyan Qing, Mo Zhou, Yuhua Zhu
arXiv:2512. 04697v3 Announce Type: replace-cross Abstract: This paper studies the continuous-time reinforcement learning (RL) for optimal switching problems across multiple regimes.
By Yijie Huang, Mengge Li, Xiang Yu, Zhou Zhou
arXiv:2606. 04275v1 Announce Type: cross Abstract: We present a novel theoretical framework for deep reinforcement learning (RL) in continuous environments by modeling the problem as a continuous-time stochastic process, drawing on insights from stochastic control.
By Saket Tiwari, Tejas Kotwal, George Konidaris
The paper investigates continuous‑time stochastic control problems with unknown drift and running reward functions, using an exploratory reinforcement learning framework that incorporates relaxed controls and entropy regularization. It develops policy‑iteration algorithms based on probabilistic representations of the optimal value function and its gradient, proving convergence and demonstrating performance through numerical examples. The study also extends to a special case with control‑dependent diffusion, requiring a Hessian representation.
By Jin Ma, Gaozhan Wang, Jianfeng Zhang, Xunyu Zhou