5 citations · 7 across the 10 of their papers we have counts for
11 papers
Decentralized Master-Mind: Joint Action Refinement through Iterative Intent Denoising in Multi-Agent Pathfinding
Valeriy Vyaltsev, Anton Andreychuk, Taisia Zlotnikova +3
Decentralized multi-agent path finding (MAPF) with communication requires agents to reach individual goals without collisions under partial observability. Learnable policies traine…
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…
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…
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…
Optimal and Bounded Suboptimal Any-Angle Multi-agent Pathfinding
Konstantin Yakovlev, Anton Andreychuk, Roni Stern
Multi-agent pathfinding (MAPF) is the problem of finding a set of conflict-free paths for a set of agents. Typically, the agents' moves are limited to a pre-defined graph of possib…
Improved Anonymous Multi-Agent Path Finding Algorithm
Zain Alabedeen Ali, Konstantin Yakovlev
We consider an Anonymous Multi-Agent Path-Finding (AMAPF) problem where the set of agents is confined to a graph, a set of goal vertices is given and each of these vertices has to…
Decentralized Monte Carlo Tree Search for Partially Observable Multi-agent Pathfinding
Alexey Skrynnik, Anton Andreychuk, Konstantin Yakovlev +1
The Multi-Agent Pathfinding (MAPF) problem involves finding a set of conflict-free paths for a group of agents confined to a graph. In typical MAPF scenarios, the graph and the age…