Wentao Huang's repositories
ADMM_Python
Solomon benchmark instances
FinRL-Library
A Deep Reinforcement Learning Library for Automated Trading in Quantitative Finance. NeurIPS 2020. Please star. 🔥
spatio-temporal-paper-list
Spatio-temporal modeling 论文列表（主要是graph convolution相关)
stable-baselines3
PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
TransportationNetworks
Transportation Networks for Research
artificial-intelligence
SEU CS Artificial Intelligence Projects
Awesome-Graph-Papers
Collect papers about graph
DQN-DDPG_Stock_Trading
Using DQN/DDPG for stock trading. Xiong, Z., Liu, X.Y., Zhong, S., Yang, H. and Walid, A., 2018. Practical deep reinforcement learning approach for stock trading, NeurIPS 2018 AI in Finance Workshop.
Excel2LaTeX
The Excel add-in for creating LaTeX tables
FindPathonMap
配合博客的一些示例程序，博客地址https://blog.csdn.net/zjgo007
FinRL-Meta
FinRL-Meta: A Universe for Data-Driven Financial Reinforcement Learning. 🔥
FPCA
Python implementation of functional PCA - model
GNN_Review
GNN综述阅读报告
machinelearning
My blogs and code for machine learning. http://cnblogs.com/pinard
matsim-example-project
A small example of how to use MATSim as a library.
Matsim_synpp
Synthetic population pipeline code for eqasim
MM-algorithm
Solve some classical regression problem by MM algorithm
Multimodal-DUE
A general formulation for multi-modal dynamic traffic assignment considering multi-class vehicles, public transit and parking
Network-learning-via-multi-agent-inverse-transportation-problems
Despite the ubiquity of transportation data, statistical inference methods alone are not able to explain mechanistic relations within a network. Inverse optimization methods that capture network structure fulfill this gap, but they are designed to take observations of the same model to learn the parameters of that model. New inverse optimization models and supporting algorithms are proposed to learn the parameters of heterogeneous travelers’ route optimization such that the value of shared network resources (e.g. link capacity dual prices) can be inferred. The inferred values are internally consistent with each agent’s optimization program. We prove that the method can obtain unique dual prices for a network shared by these agents, in polynomial time. Three experiments are conducted. The first one, conducted on a 4-node network, verifies the methodology to obtain heterogeneous link cost parameters even when a mixed logit model cannot provide meaningful results. The second is a parameter recovery test on the Nguyen-Dupuis network that shows that unique latent link capacity dual prices can be inferred using the proposed method. The last test on the same network demonstrates how a monitoring system in an online learning environment can be designed using this method.
NeuroDynamicProgramming-MFD
Source code of the Neuro-dynamic programming approach for optimal control of Macroscopic fundamental diagram (MFD) system)
Reinforcement-learning-with-tensorflow
Simple Reinforcement learning tutorials
sharecar_report
大数据看共享汽车——GoFun篇
STGCN
The PyTorch version of STGCN.
Velocity_control
Source code for paper "Safe, Efficient, and Comfortable Velocity Control based on Reinforcement Learning for Autonomous Driving"
wentaohuang.github.io
Wentao's personal website
wentaohuang2.github.io
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