2 code implementations • 29 Aug 2024 • Anton Andreychuk, Konstantin Yakovlev, Aleksandr Panov, Alexey Skrynnik
The resulting MAPF-GPT model demonstrates zero-shot learning abilities when solving the MAPF problem instances that were not present in the training dataset.
3 code implementations • 20 Jul 2024 • Alexey Skrynnik, Anton Andreychuk, Anatolii Borzilov, Alexander Chernyavskiy, Konstantin Yakovlev, Aleksandr Panov
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.
1 code implementation • 12 Jul 2024 • Zoya Volovikova, Alexey Skrynnik, Petr Kuderov, Aleksandr I. Panov
In this study, we address the issue of enabling an artificial intelligence agent to execute complex language instructions within virtual environments.
1 code implementation • 12 Jul 2024 • Shrestha Mohanty, Negar Arabzadeh, Andrea Tupini, Yuxuan Sun, Alexey Skrynnik, Artem Zholus, Marc-Alexandre Côté, Julia Kiseleva
Seamless interaction between AI agents and humans using natural language remains a key goal in AI research.
1 code implementation • 26 Dec 2023 • Alexey Skrynnik, Anton Andreychuk, Konstantin Yakovlev, Aleksandr Panov
Our approach utilizes the agent's observations to recreate the intrinsic Markov decision process, which is then used for planning with a tailored for multi-agent tasks version of neural MCTS.
1 code implementation • 5 Nov 2023 • Artem Tsypin, Leonid Ugadiarov, Kuzma Khrabrov, Alexander Telepov, Egor Rumiantsev, Alexey Skrynnik, Aleksandr I. Panov, Dmitry Vetrov, Elena Tutubalina, Artur Kadurin
Our results demonstrate that the neural network trained with GOLF performs on par with the oracle on a benchmark of diverse drug-like molecules using $50$x less additional data.
1 code implementation • 2 Oct 2023 • Alexey Skrynnik, Anton Andreychuk, Maria Nesterova, Konstantin Yakovlev, Aleksandr Panov
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.
no code implementations • 25 Jul 2023 • Yelisey Pitanov, Alexey Skrynnik, Anton Andreychuk, Konstantin Yakovlev, Aleksandr Panov
We investigate how to utilize Monte-Carlo Tree Search (MCTS) to solve the problem.
1 code implementation • 30 Dec 2022 • Maria Nesterova, Alexey Skrynnik, Aleksandr Panov
Many challenging reinforcement learning (RL) problems require designing a distribution of tasks that can be applied to train effective policies.
2 code implementations • 12 Nov 2022 • Shrestha Mohanty, Negar Arabzadeh, Milagro Teruel, Yuxuan Sun, Artem Zholus, Alexey Skrynnik, Mikhail Burtsev, Kavya Srinet, Aleksandr Panov, Arthur Szlam, Marc-Alexandre Côté, Julia Kiseleva
Human intelligence can remarkably adapt quickly to new tasks and environments.
1 code implementation • 1 Nov 2022 • Alexey Skrynnik, Zoya Volovikova, Marc-Alexandre Côté, Anton Voronov, Artem Zholus, Negar Arabzadeh, Shrestha Mohanty, Milagro Teruel, Ahmed Awadallah, Aleksandr Panov, Mikhail Burtsev, Julia Kiseleva
The adoption of pre-trained language models to generate action plans for embodied agents is a promising research strategy.
1 code implementation • PeerJ Computer Science 2022 • Alexey Skrynnik, Anton Andreychuk, Konstantin Yakovlev, Aleksandr Panov
Within planning, an agent constantly re-plans and updates the path based on the history of the observations using a search-based planner.
1 code implementation • 22 Jun 2022 • Alexey Skrynnik, Anton Andreychuk, Konstantin Yakovlev, Aleksandr I. Panov
We introduce POGEMA (https://github. com/AIRI-Institute/pogema) a sandbox for challenging partially observable multi-agent pathfinding (PO-MAPF) problems .
1 code implementation • 31 May 2022 • Artem Zholus, Alexey Skrynnik, Shrestha Mohanty, Zoya Volovikova, Julia Kiseleva, Artur Szlam, Marc-Alexandre Coté, Aleksandr I. Panov
We present the IGLU Gridworld: a reinforcement learning environment for building and evaluating language conditioned embodied agents in a scalable way.
1 code implementation • 27 May 2022 • Julia Kiseleva, Alexey Skrynnik, Artem Zholus, Shrestha Mohanty, Negar Arabzadeh, Marc-Alexandre Côté, Mohammad Aliannejadi, Milagro Teruel, Ziming Li, Mikhail Burtsev, Maartje ter Hoeve, Zoya Volovikova, Aleksandr Panov, Yuxuan Sun, Kavya Srinet, Arthur Szlam, Ahmed Awadallah
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 language instructions.
no code implementations • 5 May 2022 • Julia Kiseleva, Ziming Li, Mohammad Aliannejadi, Shrestha Mohanty, Maartje ter Hoeve, Mikhail Burtsev, Alexey Skrynnik, Artem Zholus, Aleksandr Panov, Kavya Srinet, Arthur Szlam, Yuxuan Sun, Marc-Alexandre Côté, Katja Hofmann, Ahmed Awadallah, Linar Abdrazakov, Igor Churin, Putra Manggala, Kata Naszadi, Michiel van der Meer, Taewoon Kim
The primary goal of the competition is to approach the problem of how to build interactive agents that learn to solve a task while provided with grounded natural language instructions in a collaborative environment.
no code implementations • 13 Oct 2021 • Julia Kiseleva, Ziming Li, Mohammad Aliannejadi, Shrestha Mohanty, Maartje ter Hoeve, Mikhail Burtsev, Alexey Skrynnik, Artem Zholus, Aleksandr Panov, Kavya Srinet, Arthur Szlam, Yuxuan Sun, Katja Hofmann, Michel Galley, Ahmed Awadallah
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 language instructions.
1 code implementation • 21 Sep 2021 • Leonid Ugadiarov, Alexey Skrynnik, Aleksandr I. Panov
Exploration is an essential part of reinforcement learning, which restricts the quality of learned policy.
1 code implementation • 13 Aug 2021 • Vasilii Davydov, Alexey Skrynnik, Konstantin Yakovlev, Aleksandr I. Panov
In this paper, we consider the problem of multi-agent navigation in partially observable grid environments.
1 code implementation • 17 Jun 2020 • Alexey Skrynnik, Aleksey Staroverov, Ermek Aitygulov, Kirill Aksenov, Vasilii Davydov, Aleksandr I. Panov
There are two main approaches to improving the sample efficiency of reinforcement learning methods - using hierarchical methods and expert demonstrations.
Deep Reinforcement Learning
Hierarchical Reinforcement Learning
+3
1 code implementation • 18 Dec 2019 • Alexey Skrynnik, Aleksey Staroverov, Ermek Aitygulov, Kirill Aksenov, Vasilii Davydov, Aleksandr I. Panov
We present Hierarchical Deep Q-Network (HDQfD) that took first place in the MineRL competition.