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[2602.13706] Near-Optimal Regret for Policy Optimization in Contextual MDPs with General Offline Function Approximation
Summary
This paper presents OPO-CMDP, a novel policy optimization algorithm for stochastic Contextual Markov Decision Processes (CMDPs) that achieves near-optimal regret bounds, improving upon existing methods.
Why It Matters
The development of OPO-CMDP is significant as it enhances the efficiency of policy optimization in CMDPs, which are crucial for various applications in machine learning and AI. By achieving optimal regret bounds, this research could lead to more effective decision-making processes in uncertain environments.
Key Takeaways
- OPO-CMDP is the first algorithm to optimize policy in CMDPs with general offline function approximation.
- The proposed method achieves a high probability regret bound that improves the state-of-the-art.
- The algorithm's performance is optimal concerning the size of state and action spaces.
- This research demonstrates the advantages of optimistic policy optimization in computational efficiency.
- The findings could influence future developments in decision-making frameworks in AI.
Computer Science > Machine Learning arXiv:2602.13706 (cs) [Submitted on 14 Feb 2026] Title:Near-Optimal Regret for Policy Optimization in Contextual MDPs with General Offline Function Approximation Authors:Orin Levy, Aviv Rosenberg, Alon Cohen, Yishay Mansour View a PDF of the paper titled Near-Optimal Regret for Policy Optimization in Contextual MDPs with General Offline Function Approximation, by Orin Levy and Aviv Rosenberg and Alon Cohen and Yishay Mansour View PDF HTML (experimental) Abstract:We introduce \texttt{OPO-CMDP}, the first policy optimization algorithm for stochastic Contextual Markov Decision Process (CMDPs) under general offline function approximation. Our approach achieves a high probability regret bound of $\widetilde{O}(H^4\sqrt{T|S||A|\log(|\mathcal{F}||\mathcal{P}|)}),$ where $S$ and $A$ denote the state and action spaces, $H$ the horizon length, $T$ the number of episodes, and $\mathcal{F}, \mathcal{P}$ the finite function classes used to approximate the losses and dynamics, respectively. This is the first regret bound with optimal dependence on $|S|$ and $|A|$, directly improving the current state-of-the-art (Qian, Hu, and Simchi-Levi, 2024). These results demonstrate that optimistic policy optimization provides a natural, computationally superior and theoretically near-optimal path for solving CMDPs. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2602.13706 [cs.LG] (or arXiv:2602.13706v1 [cs.LG] for this version) https://doi.org/10.48550/ar...