167 citations · 796 across the 44 of their papers we have counts for
10 papers · 2 filters
GEC: A Unified Framework for Interactive Decision Making in MDP, POMDP, and Beyond
Han Zhong, Wei Xiong, Sirui Zheng +4
We study sample efficient reinforcement learning (RL) under the general framework of interactive decision making, which includes Markov decision process (MDP), partially observable…
Rethinking Lipschitz Neural Networks and Certified Robustness: A Boolean Function Perspective
Bohang Zhang, Du Jiang, Di He +1
Designing neural networks with bounded Lipschitz constant is a promising way to obtain certifiably robust classifiers against adversarial examples. However, the relevant progress f…
Provable Sim-to-real Transfer in Continuous Domain with Partial Observations
Jiachen Hu, Han Zhong, Chi Jin +1
Sim-to-real transfer trains RL agents in the simulated environments and then deploys them in the real world. Sim-to-real transfer has been widely used in practice because it is oft…
Is Physics-Informed Loss Always Suitable for Training Physics-Informed Neural Network?
Chuwei Wang, Shanda Li, Di He +1
The Physics-Informed Neural Network (PINN) approach is a new and promising way to solve partial differential equations using deep learning. The Physics-Informed Loss is the d…
Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation
Xiaoyu Chen, Han Zhong, Zhuoran Yang +2
We study human-in-the-loop reinforcement learning (RL) with trajectory preferences, where instead of receiving a numeric reward at each step, the agent only receives preferences ov…
Nearly Minimax Optimal Offline Reinforcement Learning with Linear Function Approximation: Single-Agent MDP and Markov Game
Wei Xiong, Han Zhong, Chengshuai Shi +3
Offline reinforcement learning (RL) aims at learning an optimal strategy using a pre-collected dataset without further interactions with the environment. While various algorithms h…