4 papers · 2 filters
Near-Optimal Dynamic Regret for Adversarial Linear Mixture MDPs
Long-Fei Li, Peng Zhao, Zhi-Hua Zhou
We study episodic linear mixture MDPs with the unknown transition and adversarial rewards under full-information feedback, employing dynamic regret as the performance measure. We s…
Gradient-Variation Online Learning under Generalized Smoothness
Yan-Feng Xie, Peng Zhao, Zhi-Hua Zhou
Gradient-variation online learning aims to achieve regret guarantees that scale with variations in the gradients of online functions, which has been shown to be crucial for attaini…
Provably Efficient Reinforcement Learning with Multinomial Logit Function Approximation
Long-Fei Li, Yu-Jie Zhang, Peng Zhao +1
We study a new class of MDPs that employs multinomial logit (MNL) function approximation to ensure valid probability distributions over the state space. Despite its significant ben…
Improved Algorithm for Adversarial Linear Mixture MDPs with Bandit Feedback and Unknown Transition
Long-Fei Li, Peng Zhao, Zhi-Hua Zhou
We study reinforcement learning with linear function approximation, unknown transition, and adversarial losses in the bandit feedback setting. Specifically, we focus on linear mixt…