no code implementations • 4 Feb 2023 • Audrey Huang, Jinglin Chen, Nan Jiang
As a central technical challenge, the additive error of occupancy estimation is incompatible with the multiplicative definition of data coverage.
no code implementations • 21 Jun 2022 • Jinglin Chen, Aditya Modi, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal
We study reward-free reinforcement learning (RL) under general non-linear function approximation, and establish sample efficiency and hardness results under various standard structural assumptions.
no code implementations • 25 Mar 2022 • Jinglin Chen, Nan Jiang
We consider a challenging theoretical problem in offline reinforcement learning (RL): obtaining sample-efficiency guarantees with a dataset lacking sufficient coverage, under only realizability-type assumptions for the function approximators.
no code implementations • ICLR 2022 • Jiawei Huang, Jinglin Chen, Li Zhao, Tao Qin, Nan Jiang, Tie-Yan Liu
Deployment efficiency is an important criterion for many real-world applications of reinforcement learning (RL).
no code implementations • 14 Feb 2021 • Aditya Modi, Jinglin Chen, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal
In this work, we present the first model-free representation learning algorithms for low rank MDPs.
no code implementations • 23 Oct 2020 • Priyank Agrawal, Jinglin Chen, Nan Jiang
This paper studies regret minimization with randomized value functions in reinforcement learning.
no code implementations • 8 Oct 2020 • Huozhi Zhou, Jinglin Chen, Lav R. Varshney, Ashish Jagmohan
We consider reinforcement learning (RL) in episodic Markov decision processes (MDPs) with linear function approximation under drifting environment.
no code implementations • ICLR 2019 • Yi Chen, Jinglin Chen, Jing Dong, Jian Peng, Zhaoran Wang
To attain the advantages of both regimes, we propose to use replica exchange, which swaps between two Langevin diffusions with different temperatures.
no code implementations • 1 May 2019 • Jinglin Chen, Nan Jiang
Value-function approximation methods that operate in batch mode have foundational importance to reinforcement learning (RL).
no code implementations • 28 Oct 2017 • Jinglin Chen, Jian Peng, Qiang Liu
We propose a new localized inference algorithm for answering marginalization queries in large graphical models with the correlation decay property.