Accelerating Multi-Agent Planning Using Graph Transformers with Bounded Suboptimality

Conflict-Based Search is one of the most popular methods for multi-agent path finding. Though it is complete and optimal, it does not scale well. Recent works have been proposed to accelerate it by introducing various heuristics. However, whether these heuristics can apply to non-grid-based problem settings while maintaining their effectiveness remains an open question. In this work, we find that the answer is prone to be no. To this end, we propose a learning-based component, i. e. , the Graph Transformer, as a heuristic function to accelerate the planning. The proposed method is provably complete and bounded-suboptimal with any desired factor. We conduct extensive experiments on two environments with dense graphs. Results show that the proposed Graph Transformer can be trained in problem instances with relatively few agents and generalizes well to a larger number of agents, while achieving better performance than state-of-the-art methods.

Authors

• Chenning Yu
• Qingbiao Li
• Sicun Gao
• Amanda Prorok

Keywords

• Automation
• Transformers
• Planning
• Heuristic Algorithm
• Number Of Agents
• Problem Setting
• Computation Time
• Support Vector Machine
• Positive Samples
• Negative Samples
• Travel Time
• Feasible Solution
• Learning-based Methods
• Lowest Cost
• Solution Quality
• Vertices
• Optimal Plan
• Tree Search
• Collision Detection
• Test Instances
• Imitation Learning
• Temporal Coding
• Focal Set
• High-level Planner