A deep reinforcement learning and a dynamic graph neural network-based scheduling agent to control a multi-task robot

• A scheduling problem representative of today’s robotic-based production environments and challenges. • A mathematical formulation for a optimization model. • A framework including a simulator of the robot’s movements and a custom solving strategy based on Q-values guided beam search. • A dynamic and heterogeneous graph representation and a custom graph neural network. • A dedicated training algorithm based on reinforcement learning ( ɛ ɛ -greedy DQN).