LC-MAPF uses multi-round local communication between neighboring agents in a pre-trained model to outperform prior learning-based MAPF solvers on diverse unseen scenarios while preserving scalability.
2018 21st international conference on intelligent transportation systems (ITSC) , pages=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
A deep RL traffic light controller dynamically balances vehicle and pedestrian flows to cut congestion while delivering equitable service to both user types.
citing papers explorer
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Learning to Communicate Locally for Large-Scale Multi-Agent Pathfinding
LC-MAPF uses multi-round local communication between neighboring agents in a pre-trained model to outperform prior learning-based MAPF solvers on diverse unseen scenarios while preserving scalability.
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Balancing Efficiency and Fairness in Traffic Light Control through Deep Reinforcement Learning
A deep RL traffic light controller dynamically balances vehicle and pedestrian flows to cut congestion while delivering equitable service to both user types.