Simultaneous task and energy planning using deep reinforcement learning

•Three models are developed to study simultaneous task and energy planning problem for autonomous vehicles.•A novel neural optimization algorithm using deep reinforcement learning and a link information filter is proposed.•An end-to-end learning framework that directly maps from perceptions to control decisions is proposed.•The proposed neural optimizer can find near-optimal solutions very fast compared to exact and heuristic algorithms.