arXiv Machine Learning
1d ago

Rate-Optimal Algorithm for Adversarial Linear CMDPs

The paper introduces a new primal–dual algorithm for episodic adversarial linear constrained Markov decision processes (CMDPs) with unknown transitions. It achieves a rate‑optimal ×O(√K) regret and cumulative constraint violation, improving upon the previous ×O(K^{3/4}) bound and eliminating the need for Slater’s condition. The method combines adaptive FTRL, contracted value estimation, and an exponential Lyapunov function, enabling uniform concentration over the value function class and computational efficiency independent of the state‑space size.

By Kihyun Yu, Honghao Wei, Dabeen Lee
arXiv Machine Learning
Sep 14

A Unified and Constrained View of Regularization-Based Robust Reinforcement Learning

The paper presents a unified framework for regularization-based robust reinforcement learning by deriving upper bounds on the performance gap between nominal and worst-case policies. These bounds are expressed as a regularization objective plus a KL-divergence penalty, explaining why KL penalties enhance robustness. The authors reformulate robust training as a constrained optimization problem, updating the Lagrange multiplier jointly with the policy to automatically tune regularization, and validate the approach with extensive adversarial evaluations on continuous control tasks.

By Amine Andam, Jamal Bentahar, Mustapha Hedabou
arXiv Machine Learning
Jun 3

Data- and Variance-dependent Regret Bounds for Online Tabular MDPs

arXiv:2602. 01903v2 Announce Type: replace Abstract: This work studies online episodic tabular Markov decision processes (MDPs) with known transitions and develops best-of-both-worlds algorithms that achieve refined data-dependent regret bounds in the adversarial regime and variance-dependent regret bounds in the stochastic regime.

By Mingyi Li, Taira Tsuchiya, Kenji Yamanishi