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- Deploying Ten Thousand Robots: Scalable Imitation Learning for Lifelong Multi-Agent Path Finding28
This work proposes an imitation-learning-based LMAPF solver that introduces a novel communication module as well as systematic single-step collision resolution and global guidance techniques and inherits the fast reasoning speed of learning-based methods and the high solution quality of search-based methods with the help of modern GPUs.
- ALPHA: Attention-based Long-horizon Pathfinding in Highly-structured Areas19
This work proposes ALPHA, a new framework combining the use of ground truth proximal (local) information and fuzzy distal (global) information to let agents sequence local decisions based on the full current state of the system, and avoid such myopicity.
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- Hmr-odta: online diverse task allocation for a team of heterogeneous mobile robots3
This paper proposes a novel framework leveraging a heterogeneous robot team and an efficient dynamic scheduling algorithm that optimizes task assignments to ensure timely service while addressing delays or task rejections, and highlights the algorithm’s effectiveness in improving task scheduling and coordination in multi-robot systems.
- CAMO: A Conditional Neural Solver for the Multi-objective Multiple Traveling Salesman Problem–
CAMO is proposed, a conditional neural solver for MOMTSP that generalizes across varying numbers of targets, agents, and preference vectors, and yields high-quality approximations to the Pareto front (PF).
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- COIN: Collaborative interaction-aware multiagent reinforcement learning for self-driving systems–
This work develops a new counterfactual individual-global twin delayed deep deterministic policy gradient (CIG-TD3) algorithm, crafted in a “centralized training, decentralized execution” (CTDE) manner, which aims to jointly optimize the individual objectives (navigation) and the global objectives (collaboration) of agents.
- Social Behavior as a Key to Learning-Based Multi-Agent Pathfinding Dilemmas (Abstract Reprint)–
SYLPH is proposed, a novel learning-based MAPF framework aimed to mitigate the adverse effects of homogeneity by allowing agents to learn and dynamically select different social behaviors (akin to individual, dynamic roles), without affecting the scalability offered by parameter sharing.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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