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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.gnn_pathplanning
Graph Neural Networks for Decentralized Path Planningpopgym
Partially Observable Process Gymmagat_pathplanning
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.minicar
ros2_multi_agent_passage
HetGPPO
Heterogeneous Multi-Robot Reinforcement Learningrl_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"adversarial_comms
ffm
Reinforcement Learning with Fast and Forgetful Memorygnngls
Code accompanying the paper Graph Neural Network Guided Local Search for the Traveling Salesperson ProblemModGNN
graph-conv-memory
Graph convolutional memoryControllingBehavioralDiversity
This repository contains the code for Diversity Control (DiCo), a novel method to constrain behavioral diversity in multi-agent reinforcement learning.cambridge-robomaster
This is the source repository containing all information necessary to reproduce the Cambridge RoboMaster platform.private_flocking
task-agnostic-comms
Task-Agnostic Communication for Multi-Agent Reinforcement Learningresilient-fusion
robomaster_ros2_can
ROS2 driver to control RoboMaster S1 using the internal CAN interfacesensor-guided-visual-nav
xaer
robomaster_sdk_can
C++ library to command the RoboMaster S1 through the internal CAN busmemory-monoids
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