Hierarchical Learning With Heuristic Guidance for Multi-Task Assignment and Distributed Planning in Interactive Scenarios

A unified framework for multi-agent task assignment and distributed trajectory planning that can autonomously adapt to complex interactive environments and multi-task constraints has always been the bottleneck of unmanned cluster task. In multi-agent systems such as smart parking lots and smart intersections, tasks are time-varying with dynamically interactive agents, which cause the challenges such as inefficient assignment, spatiotemporal conflict and poor adaptive ability. Focusing on them, this paper proposes a hierarchical framework that combines centralized task assignment module with a multi-agent reinforcement learning based distributed trajectory planning module, which has a good scalability and task adaptability. The benefit of the proposed hierarchical framework is that it utilizes behavioral heuristic of congestion level and planning cost during assignment to motivate the appropriate assignment, which achieve an organic integration of multiple objectives. In terms of spatiotemporal conflicts for multi-agent, a weighted network structure is designed to capture dynamic obstacle information while introducing conflict constraints for collision avoidance policy optimization. Furthermore, in order to cope with changing tasks, re-assignment and re-planning mechanisms are incorporated into the framework, as well as the graph encoding layer which is adaptive to the uncertain tasks. Extensive experiments are conducted in autonomous parking scenarios to validate the effectiveness of the approach in task assignment and path conflict mitigation. Compared with other state of art methods, the task assignment and overall success rates have increased by 12.1% and 6.8%, respectively, with negligible computing time.