19 citations · 54 across the 20 of their papers we have counts for
25 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…
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…
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…
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…
Learn to Follow: Decentralized Lifelong Multi-agent Pathfinding via Planning and Learning
Alexey Skrynnik, Anton Andreychuk, Maria Nesterova +2
Multi-agent Pathfinding (MAPF) problem generally asks to find a set of conflict-free paths for a set of agents confined to a graph and is typically solved in a centralized fashion.…
Monte-Carlo Tree Search for Multi-Agent Pathfinding: Preliminary Results
Yelisey Pitanov, Alexey Skrynnik, Anton Andreychuk +2
In this work we study a well-known and challenging problem of Multi-agent Pathfinding, when a set of agents is confined to a graph, each agent is assigned a unique start and goal v…
TransPath: Learning Heuristics For Grid-Based Pathfinding via Transformers
Daniil Kirilenko, Anton Andreychuk, Aleksandr Panov +1
Heuristic search algorithms, e.g. A*, are the commonly used tools for pathfinding on grids, i.e. graphs of regular structure that are widely employed to represent environments in r…
Analysis Of The Anytime MAPF Solvers Based On The Combination Of Conflict-Based Search (CBS) and Focal Search (FS)
Ilya Ivanashev, Anton Andreychuk, Konstantin Yakovlev
Conflict-Based Search (CBS) is a widely used algorithm for solving multi-agent pathfinding (MAPF) problems optimally. The core idea of CBS is to run hierarchical search, when, on t…
POGEMA: Partially Observable Grid Environment for Multiple Agents
Alexey Skrynnik, Anton Andreychuk, Konstantin Yakovlev +1
We introduce POGEMA (https://github.com/AIRI-Institute/pogema) a sandbox for challenging partially observable multi-agent pathfinding (PO-MAPF) problems . This is a grid-based envi…
Towards Time-Optimal Any-Angle Path Planning With Dynamic Obstacles
Konstantin Yakovlev, Anton Andreychuk
Path finding is a well-studied problem in AI, which is often framed as graph search. Any-angle path finding is a technique that augments the initial graph with additional edges to…
Improving Continuous-time Conflict Based Search
Anton Andreychuk, Konstantin Yakovlev, Eli Boyarski +1
Conflict-Based Search (CBS) is a powerful algorithmic framework for optimally solving classical multi-agent path finding (MAPF) problems, where time is discretized into the time st…
Revisiting Bounded-Suboptimal Safe Interval Path Planning
Konstantin Yakovlev, Anton Andreychuk, Roni Stern
Safe-interval path planning (SIPP) is a powerful algorithm for finding a path in the presence of dynamic obstacles. SIPP returns provably optimal solutions. However, in many practi…
Prioritized Multi-agent Path Finding for Differential Drive Robots
Konstantin Yakovlev, Anton Andreychuk, Vitaly Vorobyev
Methods for centralized planning of the collision-free trajectories for a fleet of mobile robots typically solve the discretized version of the problem and rely on numerous simplif…
Combining Safe Interval Path Planning and Constrained Path Following Control: Preliminary Results
Konstantin Yakovlev, Anton Andreychuk, Juliya Belinskaya +1
We study the navigation problem for a robot moving amidst static and dynamic obstacles and rely on a hierarchical approach to solve it. First, the reference trajectory is planned b…
Multi-Agent Pathfinding with Continuous Time
Anton Andreychuk, Konstantin Yakovlev, Dor Atzmon +1
Multi-Agent Pathfinding (MAPF) is the problem of finding paths for multiple agents such that every agent reaches its goal and the agents do not collide. Most prior work on MAPF was…
eLIAN: Enhanced Algorithm for Angle-constrained Path Finding
Anton Andreychuk, Natalia Soboleva, Konstantin Yakovlev
Problem of finding 2D paths of special shape, e.g. paths comprised of line segments having the property that the angle between any two consecutive segments does not exceed the pred…
Multi-robot Path Planning in Well-formed Infrastructures: Prioritized Planning vs. Prioritized Wait Adjustment (Preliminary Results)
Anton Andreychuk, Konstantin Yakovlev
We study the problem of planning collision-free paths for a group of homogeneous robots. We propose a novel approach for turning the paths that were planned egocentrically by the r…
Path Finding for the Coalition of Co-operative Agents Acting in the Environment with Destructible Obstacles
Anton Andreychuk, Konstantin Yakovlev
The problem of planning a set of paths for the coalition of robots (agents) with different capabilities is considered in the paper. Some agents can modify the environment by destru…
Two Techniques That Enhance the Performance of Multi-robot Prioritized Path Planning
Anton Andreychuk, Konstantin Yakovlev
We introduce and empirically evaluate two techniques aimed at enhancing the performance of multi-robot prioritized path planning. The first technique is the deterministic procedure…
Applying MAPP Algorithm for Cooperative Path Finding in Urban Environments
Anton Andreychuk, Konstantin Yakovlev
The paper considers the problem of planning a set of non-conflict trajectories for the coalition of intelligent agents (mobile robots). Two divergent approaches, e.g. centralized a…
Any-Angle Pathfinding for Multiple Agents Based on SIPP Algorithm
Konstantin Yakovlev, Anton Andreychuk
The problem of finding conflict-free trajectories for multiple agents of identical circular shape, operating in shared 2D workspace, is addressed in the paper and decoupled, e.g.,…