Search Results for author: Alexey Skrynnik

Found 21 papers, 18 papers with code

MAPF-GPT: Imitation Learning for Multi-Agent Pathfinding at Scale

2 code implementations29 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.

Deep Reinforcement Learning Imitation Learning +1

POGEMA: A Benchmark Platform for Cooperative Multi-Agent Navigation

3 code implementations20 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.

Benchmarking Multi-agent Reinforcement Learning +1

Instruction Following with Goal-Conditioned Reinforcement Learning in Virtual Environments

1 code implementation12 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.

Instruction Following reinforcement-learning +1

Decentralized Monte Carlo Tree Search for Partially Observable Multi-agent Pathfinding

1 code implementation26 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.

Gradual Optimization Learning for Conformational Energy Minimization

1 code implementation5 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.

Drug Discovery

Learn to Follow: Decentralized Lifelong Multi-agent Pathfinding via Planning and Learning

1 code implementation2 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.

Collision Avoidance

Reinforcement Learning with Success Induced Task Prioritization

1 code implementation30 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.

reinforcement-learning Reinforcement Learning +1

Pathfinding in stochastic environments: learning vs planning

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.

POGEMA: Partially Observable Grid Environment for Multiple Agents

1 code implementation22 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 .

IGLU Gridworld: Simple and Fast Environment for Embodied Dialog Agents

1 code implementation31 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.

reinforcement-learning Reinforcement Learning +1

Long-Term Exploration in Persistent MDPs

1 code implementation21 Sep 2021 Leonid Ugadiarov, Alexey Skrynnik, Aleksandr I. Panov

Exploration is an essential part of reinforcement learning, which restricts the quality of learned policy.

Reinforcement Learning (RL)

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