Search Results for author: Alexey Skrynnik

Found 16 papers, 12 papers with code

Gradual Optimization Learning for Conformational Energy Minimization

1 code implementation5 Nov 2023 Artem Tsypin, Leonid Ugadiarov, Kuzma Khrabrov, Manvel Avetisian, 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

no code implementations2 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 (RL)

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 (RL)

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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