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    • VMAS is a vectorized differentiable simulator designed for efficient Multi-Agent Reinforcement Learning benchmarking. It is comprised of a vectorized 2D physics engine written in PyTorch and a set of challenging multi-robot scenarios. Additional scenarios can be implemented through a simple and modular interface.
      Python
      GNU General Public License v3.0
      7135062Updated Nov 27, 2024Nov 27, 2024
    • CoViS-Net

      Public
      Dataset, models and evaluation code for the paper "CoViS-Net: A Cooperative Visual Spatial Foundation Model for Multi-Robot Applications"
      Python
      GNU General Public License v3.0
      1500Updated Nov 25, 2024Nov 25, 2024
    • CMake
      0000Updated Nov 25, 2024Nov 25, 2024
    • memoroids

      Public
      Python
      0200Updated Nov 14, 2024Nov 14, 2024
    • This is the source repository containing all information necessary to reproduce the Cambridge RoboMaster platform.
      Python
      01100Updated Oct 16, 2024Oct 16, 2024
    • DVM-SLAM

      Public
      Jupyter Notebook
      42700Updated Sep 27, 2024Sep 27, 2024
    • This repository contains the code for Diversity Control (DiCo), a novel method to constrain behavioral diversity in multi-agent reinforcement learning.
      Python
      11510Updated Sep 27, 2024Sep 27, 2024
    • HetGPPO

      Public
      Heterogeneous Multi-Robot Reinforcement Learning
      Python
      133510Updated Sep 10, 2024Sep 10, 2024
    • popgym

      Public
      Partially Observable Process Gym
      Python
      MIT License
      1116711Updated Jul 4, 2024Jul 4, 2024
    • Python
      MIT License
      2910140Updated Apr 4, 2024Apr 4, 2024
    • Task-Agnostic Communication for Multi-Agent Reinforcement Learning
      Python
      0500Updated Mar 31, 2024Mar 31, 2024
    • ROS2 driver to control RoboMaster S1 using the internal CAN interface
      C++
      0300Updated Mar 6, 2024Mar 6, 2024
    • Python
      0310Updated Oct 5, 2023Oct 5, 2023
    • This is a minimal example to demonstrate how multi-agent reinforcement learning with differentiable communication channels and centralized critics can be realized in RLLib. This example serves as a reference implementation and starting point for making RLLib more compatible with such architectures.
      Python
      34021Updated Sep 24, 2023Sep 24, 2023
    • A simple ROS2 point simulator
      Python
      1000Updated Aug 17, 2023Aug 17, 2023
    • ffm

      Public
      Reinforcement Learning with Fast and Forgetful Memory
      Python
      MIT License
      02300Updated Aug 15, 2023Aug 15, 2023
    • gnngls

      Public
      Code accompanying the paper Graph Neural Network Guided Local Search for the Traveling Salesperson Problem
      Python
      92520Updated Feb 19, 2023Feb 19, 2023
    • xaer

      Public
      Python
      MIT License
      1200Updated Jun 8, 2022Jun 8, 2022
    • Python
      0000Updated Jun 6, 2022Jun 6, 2022
    • Graph convolutional memory
      Python
      11500Updated May 26, 2022May 26, 2022
    • Python
      1300Updated May 18, 2022May 18, 2022
    • Repository containing RL environment, model and trainer for GNN demo for ICRA 2022 paper "A Framework for Real-World Multi-Robot Systems\\Running Decentralized GNN-Based Policies"
      Python
      GNU General Public License v3.0
      83300Updated Mar 21, 2022Mar 21, 2022
    • Python
      GNU General Public License v3.0
      133710Updated Mar 3, 2022Mar 3, 2022
    • C++ library to command the RoboMaster S1 through the internal CAN bus
      C++
      0300Updated Feb 22, 2022Feb 22, 2022
    • TeX
      6800Updated Sep 9, 2021Sep 9, 2021
    • Graph Neural Networks for Decentralized Path Planning
      Python
      MIT License
      3420040Updated Jun 29, 2021Jun 29, 2021
    • Python
      GNU General Public License v3.0
      43050Updated Apr 25, 2021Apr 25, 2021
    • ModGNN

      Public
      Python
      GNU General Public License v3.0
      11600Updated Mar 26, 2021Mar 26, 2021
    • minicar

      Public
      Python
      123720Updated Apr 10, 2020Apr 10, 2020