3 citations · 7 across the 6 of their papers we have counts for
6 papers
Settling the Horizon-Dependence of Sample Complexity in Reinforcement Learning
Yuanzhi Li, Ruosong Wang, Lin F. Yang
Recently there is a surge of interest in understanding the horizon-dependence of the sample complexity in reinforcement learning (RL). Notably, for an RL environment with horizon l…
Breaking the Moments Condition Barrier: No-Regret Algorithm for Bandits with Super Heavy-Tailed Payoffs
Han Zhong, Jiayi Huang, Lin F. Yang +1
Despite a large amount of effort in dealing with heavy-tailed error in machine learning, little is known when moments of the error can become non-existential: the random noise …
Theoretically Principled Deep RL Acceleration via Nearest Neighbor Function Approximation
Junhong Shen, Lin F. Yang
Recently, deep reinforcement learning (RL) has achieved remarkable empirical success by integrating deep neural networks into RL frameworks. However, these algorithms often require…
Safe Reinforcement Learning with Linear Function Approximation
Sanae Amani, Christos Thrampoulidis, Lin F. Yang
Safety in reinforcement learning has become increasingly important in recent years. Yet, existing solutions either fail to strictly avoid choosing unsafe actions, which may lead to…
Randomized Exploration for Reinforcement Learning with General Value Function Approximation
Haque Ishfaq, Qiwen Cui, Viet Nguyen +5
We propose a model-free reinforcement learning algorithm inspired by the popular randomized least squares value iteration (RLSVI) algorithm as well as the optimism principle. Unlik…
Provably Correct Optimization and Exploration with Non-linear Policies
Fei Feng, Wotao Yin, Alekh Agarwal +1
Policy optimization methods remain a powerful workhorse in empirical Reinforcement Learning (RL), with a focus on neural policies that can easily reason over complex and continuous…