5 papers
Learning to Communicate Locally for Large-Scale Multi-Agent Pathfinding
Valeriy Vyaltsev, Alsu Sagirova, Anton Andreychuk +5
Multi-agent pathfinding (MAPF) is a widely used abstraction for multi-robot trajectory planning problems, where multiple homogeneous agents move simultaneously within a shared envi…
MARL-GPT: Foundation Model for Multi-Agent Reinforcement Learning
Maria Nesterova, Mikhail Kolosov, Anton Andreychuk +6
Recent advances in multi-agent reinforcement learning (MARL) have demonstrated success in numerous challenging domains and environments, but typically require specialized models fo…
Advancing Learnable Multi-Agent Pathfinding Solvers with Active Fine-Tuning
Anton Andreychuk, Konstantin Yakovlev, Aleksandr Panov +1
Multi-agent pathfinding (MAPF) is a common abstraction of multi-robot trajectory planning problems, where multiple homogeneous robots simultaneously move in the shared environment.…
MAPF-GPT: Imitation Learning for Multi-Agent Pathfinding at Scale
Anton Andreychuk, Konstantin Yakovlev, Aleksandr Panov +1
Multi-agent pathfinding (MAPF) is a problem that generally requires finding collision-free paths for multiple agents in a shared environment. Solving MAPF optimally, even under res…
POGEMA: A Benchmark Platform for Cooperative Multi-Agent Pathfinding
Alexey Skrynnik, Anton Andreychuk, Anatolii Borzilov +3
Multi-agent reinforcement learning (MARL) has recently excelled in solving challenging cooperative and competitive multi-agent problems in various environments, typically involving…