sxhxhg

sxhxhg's Stars

• kaixindelele/ChatPaper

  Use ChatGPT to summarize the arXiv papers. 全流程加速科研，利用chatgpt进行论文全文总结+专业翻译+润色+审稿+审稿回复

  Language:Python18.5k942181.9k

• opendilab/PPOxFamily

  PPO x Family DRL Tutorial Course（决策智能入门级公开课：8节课帮你盘清算法理论，理顺代码逻辑，玩转决策AI应用实践 ）

  Language:Python2k1717179

• huawei-noah/SMARTS

  Scalable Multi-Agent RL Training School for Autonomous Driving

  Language:Python958131k190

• cityflow-project/CityFlow

  A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario

  Language:C++79719135173

• marlbenchmark/off-policy

  PyTorch implementations of popular off-policy multi-agent reinforcement learning algorithms, including QMix, VDN, MADDPG, and MATD3.

  Language:Python41631269

• zhaohaojie1998/Grey-Wolf-Optimizer-for-Path-Planning

  灰狼优化算法(GWO)路径规划/轨迹规划/轨迹优化、多智能体/多无人机航迹规划

  Language:MATLAB4123735

• cts198859/deeprl_signal_control

  multi-agent deep reinforcement learning for large-scale traffic signal control.

  Language:Python351123586

• PKU-MARL/Multi-Agent-Transformer

  Language:Python34484071

• schroederdewitt/multiagent_mujoco

  Benchmark for Continuous Multi-Agent Robotic Control, based on OpenAI's Mujoco Gym environments.

  Language:Python33282134

• semitable/robotic-warehouse

  Multi-Robot Warehouse (RWARE): A multi-agent reinforcement learning environment

  Language:Python31752472

• TimeBreaker/MARL-papers-with-code

  Multi-Agent Reinforcement Learning (MARL) papers with code

  3138038

• DongChen06/MARL_CAVs

  MARL for Autonomous Vehicles

  Language:Python25665648

• opendilab/ACE

  [AAAI 2023] Official PyTorch implementation of paper "ACE: Cooperative Multi-agent Q-learning with Bidirectional Action-Dependency".

  Language:Python2195610

• binary-husky/hmp2g

  Multiagent Reinforcement Learning Research Project

  Language:Python1185834

• Kyushik/Unity_ML_Agent

  This repository is for Reinforcement Learning algorithms with Unity ML Agent

  Language:C#1066223

• SJTUwbl/MaCA

  Multi-agent Combat Arena (UAV swarm vs UAV swarm)

  Language:Python1035623

• zoeyuchao/mappo

  This is the official implementation of Multi-Agent PPO.

  Language:Python941019

• AlirezaShamsoshoara/Reinforcement_Learning_Team_Q_learnig_MARL_Multi_Agent_UAV_Spectrum_task

  A solution for Dynamic Spectrum Management in Mission-Critical UAV Networks using Team Q learning as a Multi-Agent Reinforcement Learning Approach

  Language:Python612118

• Linaom1214/RL_air-combat

  基于强化学习的空战对抗

  Language:Python613110

• mahmoudtaouti/RL_Highway_Merge

  applying multi-agent reinforcement learning for highway-merging autonomous vehicles

  Language:Python45231

• colaforced/AgentsMotionSimulation

  多智能体均匀多边形编队、追逐与合围。

  Language:Python43101

• eyextent/AirCombateRL

  Language:Python381319

• LAMDA-RL/ODIS

  The implementation of ICLR-2023 paper "Discovering Generalizable Multi-agent Coordination Skills from Multi-task Offline Data".

  Language:Python38145

• SJTUwbl/multi-UAV

  Language:Python26144

• xihuai18/A2PO-ICLR2023

  Codebase for [Order Matters: Agent-by-agent Policy Optimization](https://openreview.net/forum?id=Q-neeWNVv1)

  Language:Python26121

• garrett4wade/revisiting_marl

  Official codebase for paper "Revisiting Some Common Practices in Cooperative Multi-Agent Reinforcement Learning" (ICML22)

  Language:Python21211

• Fzk1203/MARL_UAV

  This repository implements the proposed method from IEEE 2020 Paper Multi-Agent Reinforcement Learning Based Resource Allocation for UAV Networks

  Language:Jupyter Notebook20113

• MuhammetAkcann/3d-manuver-decision-in-air-combat-situations

  3d manuver decision in air combat situations

  Language:Python14113

• CCCCCCCCCChen/Multi-aircraft-conflict-resolution-based-on-multi-agent-reinforcement-learning

  The source code is the entry for 2022 EUROCONTROL Air Transport Innovation Network Innovation Masterclass. The reinforcement learning model and algorithm are provide by the team of Cranfield University (Yutong Chen, with Rodolphe Fremond, Arinc Altun, Dr. Mehmet Hasanzade, Dr. Yan Xu, and Prof. Gokhan Inalhan). The model is an extention of the study by the team of Nanjing University of Aeronautics and Astronautics(Yutong Chen, with Lei Yang, Hua Xie and Minghua Hu) The simulation environment is provided by EUROCONTROL (Ramon Dalmau-Codina). The source code is only for study and communication.

  Language:Python12100

• garrett4wade/Trust-Region-Methods-in-Multi-Agent-Reinforcement-Learning

  Language:Python30