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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
PublicDVM-SLAM
Publicros2_robomaster_msgs
Publicmemoroids
Publiccambridge-robomaster
PublicHetGPPO
PublicHeterogeneous Multi-Robot Reinforcement Learning- Partially Observable Process Gym
task-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.
xaer
Publicgraph-conv-memory
Publicresilient-fusion
Publicrl_multi_agent_passage
Publicprivate_flocking
Publicgnn_pathplanning
Publicadversarial_comms
PublicModGNN
Publicminicar
Public