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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.
CoViS-Net
Publicmemoroids
Publiccambridge-robomaster
PublicDVM-SLAM
PublicHetGPPO
PublicHeterogeneous Multi-Robot Reinforcement Learning- Partially Observable Process Gym
magat_pathplanning
Publictask-agnostic-comms
Publicrobomaster_ros2_can
Publicsensor-guided-visual-nav
Public- 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.
ffm
Publicgnngls
Publicxaer
Publicros2_infrastructure
Publicgraph-conv-memory
Publicresilient-fusion
Publicrl_multi_agent_passage
Publicros2_multi_agent_passage
Publicrobomaster_sdk_can
Publicprivate_flocking
Publicgnn_pathplanning
Publicadversarial_comms
PublicModGNN
Publicminicar
Public