Solving the Shepherding Problem: Imitation Learning Can Acquire the Switching Algorithm

• Main Authors: Go, Clark Kendrick C, Koganti, Nishanth, Ikeda, Kazushi
• Format: text
• Published: Archīum Ateneo 2021
• Subjects: Adaptation models; Heuristic algorithms; Neural networks; Switches; Dogs; Reinforcement learning; Logic gates; imitation learning; herding; Mathematics
• Online Access: https://archium.ateneo.edu/mathematics-faculty-pubs/180; https://ieeexplore.ieee.org/document/9533722
• Institution: Ateneo De Manila University

A single shepherd dog can herd a flock of sheep to a gate. Despite a heuristic algorithm of a dog based on adaptive switching between collecting the sheep when they are too dispersed and driving them once they are aggregated, it remains unknown how the dog learns the algorithm of switching. In fact, reinforcement learning models have not succeeded so far in reproducing the switching algorithm without explicitly making two strategies. Here, we show that an imitation learning model can reproduce the switching algorithm, that is, the dog learns the algorithm from demonstrations by an expert. We also confirmed that the dog does not simply copy the demonstrations but learns the required task by showing that it can herd more sheep than those in the given demonstrations.