We lifted 2 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| kaist-silab/himap | canonical | 2 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| load_map_data | Ran | kaist-silab/himap/src/utils/maps.py pointer only (licence: NONE) · get_code("9892d78b49c89c2e") |
| normalized_columns_initializer | Ran | kaist-silab/himap/src/models/vggnet.py pointer only (licence: NONE) · get_code("157c2af2dc5c2860") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Large-scale multi-agent pathfinding (MAPF) presents significant challenges in several areas. As systems grow in complexity with a multitude of autonomous agents operating simultaneously, efficient and collision-free coordination becomes paramount. Traditional algorithms often fall short in scalability, especially in intricate scenarios. Reinforcement Learning (RL) has shown potential to address the intricacies of MAPF; however, it has also been shown to struggle with scalability, demanding intricate implementation, lengthy training, and often exhibiting unstable convergence, limiting its practical application. In this paper, we introduce Heuristics-Informed Multi-Agent Pathfinding (HiMAP), a novel scalable approach that employs imitation learning with heuristic guidance in a decentralized manner. We train on small-scale instances using a heuristic policy as a teacher that maps each single agent observation information to an action probability distribution. During pathfinding, we adopt several inference techniques to improve performance. With a simple training scheme and implementation, HiMAP demonstrates competitive results in terms of success rate and scalability in the field of imitation-learning-only MAPF, showing the potential of imitation-learning-only MAPF equipped with inference techniques.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2402.15546")
get_code_for_paper("2402.15546")
have("2402.15546")
Connect an agent — have() is free.