Planning

We present a novel algorithm (DeepMNavigate) for global multi-agent navigation in dense scenarios using deep reinforcement learning. Our approach uses local and global information for each robot based on motion information maps. We demonstrate the performance on complex, dense benchmarks with narrow passages on environments with tens of agents. We highlight the algorithm’s benefits over prior learning methods and geometric decentralized algorithms in complex scenarios.

In this project, we want to build a planner for multi-robot system, using local and global information and dividing policy decision process into two phases. We want to leverage the power of reinforcement learning, and train the policy in a simulation scenario, instead of using real-word training data.