5 papers
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…
Decentralized Unlabeled Multi-agent Pathfinding Via Target And Priority Swapping (With Supplementary)
Stepan Dergachev, Konstantin Yakovlev
In this paper we study a challenging variant of the multi-agent pathfinding problem (MAPF), when a set of agents must reach a set of goal locations, but it does not matter which ag…
Safe Policy Exploration Improvement via Subgoals
Brian Angulo, Gregory Gorbov, Aleksandr Panov +1
Reinforcement learning is a widely used approach to autonomous navigation, showing potential in various tasks and robotic setups. Still, it often struggles to reach distant goals w…