Research Resources on Human-Machine Collaborative Sensing by AI-Driven Unmanned Vehicles（边缘环境人机协同群智感知与决策关键技术研究）

Code for paper "Computation Offloading Optimization for UAV-assisted Mobile Edge Computing: A Deep Deterministic Policy Gradient Approach"

Computational framework for reinforcement learning in traffic control

计算机自学指南

Clean, Robust, and Unified PyTorch implementation of popular Deep Reinforcement Learning (DRL) algorithms (Q-learning, Duel DDQN, PER, C51, Noisy DQN, PPO, DDPG, TD3, SAC, ASL)

Learning to Communicate with Deep Multi-Agent Reinforcement Learning in PyTorch

Implementation of: Nazari, Mohammadreza, et al. "Deep Reinforcement Learning for Solving the Vehicle Routing Problem." arXiv preprint arXiv:1802.04240 (2018).

An elegant PyTorch deep reinforcement learning library.

A collection of MARL benchmarks based on TorchRL

Paper list of Reinforcement Learning (RL) applied on transportation

High-performance RL library for transportation problems, e.g., autonomous driving, traffic light control, UAV control, and path planning.