
CRAMP introduces a crowd-aware decentralized reinforcement learning approach using Graph Neural Networks to enhance multi-agent path finding in congested environments.


coremsg

optimizing-crowd-aware-multi-agent-path-finding-with-cramp-approach


Optimizing Crowd-Aware Multi-Agent Path Finding with CRAMP Approach


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This research paper introduces novel local search methods based on dynamic programming to enhance the efficiency of sub-optimal solutions for Multi-Agent Path Finding (MAPF) problems on directed graphs.


improving-multi-agent-path-finding-solutions-on-directed-graphs-using-dynamic-programming-and-local-search


Improving Multi-Agent Path Finding Solutions on Directed Graphs Using Dynamic Programming and Local Search



本稿では、有向グラフ上のMulti-Agent Path Finding (MAPF) 問題に対して、既知の実行可能解を改善するための新しい局所探索手法を提案する。動的計画法を用いて近傍を探索することで、計算量を抑えつつ、より短い解を見つけることを目指す。


有向グラフ上のmulti-agent-path-finding問題に対する動的計画法に基づく局所探索アプローチ


有向グラフ上のMulti-Agent Path Finding問題に対する動的計画法に基づく局所探索アプローチ
