Zobrazeno 1 - 10
of 44
pro vyhledávání: '"Skrynnik, Alexey"'
Multi-agent pathfinding (MAPF) is a challenging computational problem that typically requires to find collision-free paths for multiple agents in a shared environment. Solving MAPF optimally is NP-hard, yet efficient solutions are critical for numero
Externí odkaz:
http://arxiv.org/abs/2409.00134
Autor:
Skrynnik, Alexey, Andreychuk, Anton, Borzilov, Anatolii, Chernyavskiy, Alexander, Yakovlev, Konstantin, Panov, Aleksandr
Multi-agent reinforcement learning (MARL) has recently excelled in solving challenging cooperative and competitive multi-agent problems in various environments with, mostly, few agents and full observability. Moreover, a range of crucial robotics-rel
Externí odkaz:
http://arxiv.org/abs/2407.14931
In this study, we address the issue of enabling an artificial intelligence agent to execute complex language instructions within virtual environments. In our framework, we assume that these instructions involve intricate linguistic structures and mul
Externí odkaz:
http://arxiv.org/abs/2407.09287
Autor:
Mohanty, Shrestha, Arabzadeh, Negar, Tupini, Andrea, Sun, Yuxuan, Skrynnik, Alexey, Zholus, Artem, Côté, Marc-Alexandre, Kiseleva, Julia
Seamless interaction between AI agents and humans using natural language remains a key goal in AI research. This paper addresses the challenges of developing interactive agents capable of understanding and executing grounded natural language instruct
Externí odkaz:
http://arxiv.org/abs/2407.08898
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 agents' starting and ending vertices are known beforehand, allowing the u
Externí odkaz:
http://arxiv.org/abs/2312.15908
Autor:
Tsypin, Artem, Ugadiarov, Leonid, Khrabrov, Kuzma, Telepov, Alexander, Rumiantsev, Egor, Skrynnik, Alexey, Panov, Aleksandr I., Vetrov, Dmitry, Tutubalina, Elena, Kadurin, Artur
Molecular conformation optimization is crucial to computer-aided drug discovery and materials design. Traditional energy minimization techniques rely on iterative optimization methods that use molecular forces calculated by a physical simulator (orac
Externí odkaz:
http://arxiv.org/abs/2311.06295
Autor:
Skrynnik, Alexey, Andreychuk, Anton, Nesterova, Maria, Yakovlev, Konstantin, Panov, Aleksandr
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. Conversely, in this work, we investigate the decentralized MAPF setti
Externí odkaz:
http://arxiv.org/abs/2310.01207
Autor:
Pitanov, Yelisey, Skrynnik, Alexey, Andreychuk, Anton, Yakovlev, Konstantin, Panov, Aleksandr
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 vertices and the task is to find a set of collision-free paths (one for
Externí odkaz:
http://arxiv.org/abs/2307.13453
Publikováno v:
MICAI 2022. Lecture Notes in Computer Science, vol 13612
Many challenging reinforcement learning (RL) problems require designing a distribution of tasks that can be applied to train effective policies. This distribution of tasks can be specified by the curriculum. A curriculum is meant to improve the resul
Externí odkaz:
http://arxiv.org/abs/2301.00691
Autor:
Mohanty, Shrestha, Arabzadeh, Negar, Teruel, Milagro, Sun, Yuxuan, Zholus, Artem, Skrynnik, Alexey, Burtsev, Mikhail, Srinet, Kavya, Panov, Aleksandr, Szlam, Arthur, Côté, Marc-Alexandre, Kiseleva, Julia
Publikováno v:
Interactive Learning for Natural Language Processing NeurIPS 2022 Workshop
Human intelligence can remarkably adapt quickly to new tasks and environments. Starting from a very young age, humans acquire new skills and learn how to solve new tasks either by imitating the behavior of others or by following provided natural lang
Externí odkaz:
http://arxiv.org/abs/2211.06552