This is a simulation targeting the bus bunching problem, including both traditional control methods and RL (Reinforcement Learning) control methods.

bus bunching visualization

An offline deep reinforcement learning library

An elegant PyTorch offline reinforcement learning library for researchers.

JupyterHub Authenticator that lets users set passwords when they first log in

清华大学出版社《Python从入门到提高》源代码、课件

Reinforcement Learning environments for Traffic Signal Control with SUMO. Compatible with Gymnasium, PettingZoo, and popular RL libraries.

SUMO Tutorial, including net, route, traffic light, detector, sumolib and traci.

Simulation program for the paper titled "Impacts of Bus Overtaking Policies on the Capacity of Bus Stops".

A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training

Constrained optimization toolkit for PyTorch

Implementations of IQL, QMIX, VDN, COMA, QTRAN, MAVEN, CommNet, DyMA-CL, and G2ANet on SMAC, the decentralised micromanagement scenario of StarCraft II

The basic distribution probability Tutorial for Deep Learning Researchers

分享 GitHub 上有趣、入门级的开源项目。Share interesting, entry-level open source projects on GitHub.

Undergraduate graduation project: Spatial-Temporal Data Analysis based on Graph Convolution Network

超级速查表 - 编程语言、框架和开发工具的速查表，单个文件包含一切你需要知道的东西 ⚡

A Julia package for handling the Next Generation Simulation (NGSIM) traffic dataset

Efficient Large-Scale Multi-Drone Delivery Using Transit Networks

Computational framework for reinforcement learning in traffic control

A collection of multi agent environments based on OpenAI gym.

A Python-embedded modeling language for convex optimization problems.

rllab is a framework for developing and evaluating reinforcement learning algorithms, fully compatible with OpenAI Gym.

Deep Learning and Reinforcement Learning Library for Scientists and Engineers

Auxiliary code to my PyCon Sweden 2017 talk "Design Patterns in Python"

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Advers…

A implementation of SeqGAN in PyTorch, following the implementation in tensorflow.

provides a modeling interface for mixed complementarity problems (MCP) and math programs with equilibrium problems (MPEC) via JuMP

Code for a multi-agent particle environment used in the paper "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments"

A collection of multi-agent reinforcement learning OpenAI gym environments

Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.