杨奕彬's repositories
Car-like-Robotic-swarm
Source code for the decentralized car-like robotic swarm
CL-CBS
An Efficient Multi-Agent Path Finding Solver for Car-Like Robots
Cpp-Primer-Plus-6th
《C++ Primer Plus 第6版（中文版）》原书代码、习题答案和个人笔记，仅供学习和交流。
Cpp-tutorial
清华大学电子工程系软件部暑培——C++
CS-Notes
:books: 技术面试必备基础知识、Leetcode、计算机操作系统、计算机网络、系统设计
CSDOTrajectoryPlanning
Efficient Multi-Vehicle Trajectory Planning via Centralized Searching Decentralized Optimization
docker_learning
learning scripts
fiss_planner
[RA-L 2022] FISS: A Trajectory Planning Framework using Fast Iterative Search and Sampling Strategy for Autonomous Driving
gpir
[ICRA'22] Real-Time Trajectory Planning for Autonomous Driving with Gaussian Process and Incremental Refinement
libMultiRobotPlanning
Library with search algorithms for task and path planning for multi robot/agent systems
MAAC
Code for "Actor-Attention-Critic for Multi-Agent Reinforcement Learning" ICML 2019
MAIC
The implementation of AAAI'22 paper "Multi-Agent Incentive Communication via Decentralized Teammate Modeling".
MAPF-LNS2
MAPF-LNS2: Fast Repairing for Multi-Agent Path Finding via Large Neighborhood Search
MARL-Papers
Paper list of multi-agent reinforcement learning (MARL)
MAVEN
Submission for MAVEN: Multi-Agent Variational Exploration
MotionPlanning
Motion planning algorithms commonly used on autonomous vehicles. (path planning + path tracking)
multi-robot-trajectory-planning
Hybrid motion planner related to "Trajectory Planning for Quadrotor Swarms" and "Trajectory Planning for Heterogeneous Robot Teams" papers
multi_robot_traj_planner
An Efficient Multi-Robot Trajectory Planner for Ground Vehicles.
pymarl
Python Multi-Agent Reinforcement Learning framework
teb_local_planner
An optimal trajectory planner considering distinctive topologies for mobile robots based on Timed-Elastic-Bands (ROS Package)
ToM2C
The offcial implementation of "ToM2C: Target-oriented Multi-agent Communication and Cooperation with Theory of Mind" (ICLR 2022) .
VBC
pytorch implementation of "Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Control"