25 citations · 98 across the 22 of their papers we have counts for
5 papers · 1 filter
Truncated Variance Reduced Value Iteration
Yujia Jin, Ishani Karmarkar, Aaron Sidford +1
We provide faster randomized algorithms for computing an -optimal policy in a discounted Markov decision process with -state-action pairs, bounded rewards, and d…
VOL: Towards Optimal Regret in Model-free RL with Nonlinear Function Approximation
Alekh Agarwal, Yujia Jin, Tong Zhang
We study time-inhomogeneous episodic reinforcement learning (RL) under general function approximation and sparse rewards. We design a new algorithm, Variance-weighted Optimistic $Q…
A Unified Framework for Multi-distribution Density Ratio Estimation
Lantao Yu, Yujia Jin, Stefano Ermon
Binary density ratio estimation (DRE), the problem of estimating the ratio given their empirical samples, provides the foundation for many state-of-the-art machine learni…
Towards Tight Bounds on the Sample Complexity of Average-reward MDPs
Yujia Jin, Aaron Sidford
We prove new upper and lower bounds for sample complexity of finding an -optimal policy of an infinite-horizon average-reward Markov decision process (MDP) given access to a gen…
Efficiently Solving MDPs with Stochastic Mirror Descent
Yujia Jin, Aaron Sidford
We present a unified framework based on primal-dual stochastic mirror descent for approximately solving infinite-horizon Markov decision processes (MDPs) given a generative model.…