Follow
Xingdong Zuo
Title
Cited by
Cited by
Year
Direct preference-based policy optimization without reward modeling
G An, J Lee, X Zuo, N Kosaka, KM Kim, HO Song
Advances in Neural Information Processing Systems 36, 70247-70266, 2023
182023
mazelab: A customizable framework to create maze and gridworld environments.
X Zuo
https://github.com/zuoxingdong/mazelab, 2018
112018
HyperCLOVA X Technical Report
KM Yoo, J Han, S In, H Jeon, J Jeong, J Kang, H Kim, KM Kim, M Kim, ...
arXiv preprint arXiv:2404.01954, 2024
32024
PyTorch implementation of Improving PILCO with Bayesian neural network dynamics models
X Zuo
https://github.com/zuoxingdong/DeepPILCO, 2018
3*2018
Numerical simulation of Asano-Khrennikov-Ohya quantum-like decision making model
X Zuo
NeuroQuantology 12 (4), 2014
22014
Designing an offline reinforcement learning objective from scratch.
G An, J Lee, X Zuo, N Kosaka, KM Kim, HO Song
arXiv preprint arXiv:2301.12842, 2023
12023
Is Learning World Model Always Beneficial For Reinforcement Learning?
X Zuo
http://dx.doi.org/10.13140/RG.2.2.33768.80641, 2021
2021
recsys_metrics: An efficient PyTorch implementation of the evaluation metrics in recommender systems
X Zuo
https://github.com/zuoxingdong/recsys_metrics, 2021
2021
gym-recsys: Customizable RecSys Simulator for OpenAI Gym
X Zuo
https://github.com/zuoxingdong/gym-recsys, 2021
2021
dm2gym: Convert DeepMind Control Suite to OpenAI gym environments.
X Zuo
https://github.com/zuoxingdong/dm2gym, 2019
2019
lagom: A light PyTorch infrastructure to quickly prototype reinforcement learning algorithms
X Zuo
https://github.com/zuoxingdong/lagom, 2018
2018
Derivation of the Lindblad Equation for Open Quantum Systems and Its Application to Mathematical Modeling of the Process of Decision Making
X Zuo
2014
Technical Report: Theoretical Framework for temporal abstraction in reinforcement learning
X Zuo
The system can't perform the operation now. Try again later.
Articles 1–13