Human-Drone Collaborative Spatial Crowdsourcing by Memory-Augmented and Distributed Multi-Agent Deep Reinforcement Learning

Spatial crowdsourcing (SC) has been proved quite successful by employing human participants to achieve certain tasks like Uber and Gigwalk. Meanwhile, with the fast devel-opment of unmanned aerial vehicles (e.g., drones), they have become a new source of data collectors equipped with a variety of different sensors. In this paper, we propose a novel SC scenario, enabling human participants to work collaboratively with drones in the presence of multiple charging stations to achieve certain data collection tasks, like videography and surveillance. We propose a novel deep reinforcement learning (D RL) framework called 'FD- MAPPO (Cubic Map)', which consists of a fully de-centralized multi-agent DRL (MADRL) algorithm called 'Fully Decentralized Multi-Agent Proximal Policy Optimization (FD-MAPPO)', and a spatiotemporal memory augmented neural network with novel cubic writing and spatially contextual reading mechanisms called 'Cubic Map'. Cubic Map extracts long-term spatiotemporal features, navigates drones to accurately locate the position of the target, i.e., charging stations or sensors. Extensive results on two real datasets of KAIST and NCSU campuses show that FD- MAPPO (Cubic Map) consistently outperforms six other baselines in terms of efficiency.