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adversarial_comms

cambridge-robomaster

ffm

Reinforcement Learning with Fast and Forgetful Memory

gnn_pathplanning

Graph Neural Networks for Decentralized Path Planning

gnngls

Code accompanying the paper Graph Neural Network Guided Local Search for the Traveling Salesperson Problem

graph-conv-memory

Graph convolutional memory

hetgppo

Heterogeneous Multi-Robot Reinforcement Learning

magat_pathplanning

memory-monoids

minicar

modgnn

popgym

Partially Observable Process Gym

private_flocking

resilient-fusion

rl_multi_agent_passage

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"

rllib_differentiable_comms

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.

robomaster_ros2_can

ROS2 driver to control RoboMaster S1 using the internal CAN interface

robomaster_sdk_can

C++ library to command the RoboMaster S1 through the internal CAN bus

ros2_infrastructure

ros2_multi_agent_passage

ros2_point_robot_simulator

A simple ROS2 point simulator

ros2_robomaster_msgs

sensor-guided-visual-nav

task-agnostic-comms

Task-Agnostic Communication for Multi-Agent Reinforcement Learning

vectorizedmultiagentsimulator

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.

xaer