4 papers · 1 filter
uniINF: Best-of-Both-Worlds Algorithm for Parameter-Free Heavy-Tailed MABs
Yu Chen, Jiatai Huang, Yan Dai +1
In this paper, we present a novel algorithm, uniINF, for the Heavy-Tailed Multi-Armed Bandits (HTMAB) problem, demonstrating robustness and adaptability in both stochastic and adve…
Adversarial Network Optimization under Bandit Feedback: Maximizing Utility in Non-Stationary Multi-Hop Networks
Yan Dai, Longbo Huang
Stochastic Network Optimization (SNO) concerns scheduling in stochastic queueing systems. It has been widely studied in network theory. Classical SNO algorithms require network con…
Provable Risk-Sensitive Distributional Reinforcement Learning with General Function Approximation
Yu Chen, Xiangcheng Zhang, Siwei Wang +1
In the realm of reinforcement learning (RL), accounting for risk is crucial for making decisions under uncertainty, particularly in applications where safety and reliability are pa…
Provably Efficient Partially Observable Risk-Sensitive Reinforcement Learning with Hindsight Observation
Tonghe Zhang, Yu Chen, Longbo Huang
This work pioneers regret analysis of risk-sensitive reinforcement learning in partially observable environments with hindsight observation, addressing a gap in theoretical explora…