6 citations · 44 across the 28 of their papers we have counts for
8 papers · 1 filter
VLA: On Recurrent Memory for Partially Observable Manipulation in VLA Models
Egor Cherepanov, Nikita Kachaev, Daniil Zelezetsky +6
Vision-language-action (VLA) models predict chunks of future actions from the current observation, an assumption that fails under partial observability, where decisions depend on i…
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…
Reinforcement Learning with Success Induced Task Prioritization
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. This distribution of tasks can be s…
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…
IGLU Gridworld: Simple and Fast Environment for Embodied Dialog Agents
Artem Zholus, Alexey Skrynnik, Shrestha Mohanty +5
We present the IGLU Gridworld: a reinforcement learning environment for building and evaluating language conditioned embodied agents in a scalable way. The environment features vis…
Long-Term Exploration in Persistent MDPs
Leonid Ugadiarov, Alexey Skrynnik, Aleksandr I. Panov
Exploration is an essential part of reinforcement learning, which restricts the quality of learned policy. Hard-exploration environments are defined by huge state space and sparse…
Q-Mixing Network for Multi-Agent Pathfinding in Partially Observable Grid Environments
Vasilii Davydov, Alexey Skrynnik, Konstantin Yakovlev +1
In this paper, we consider the problem of multi-agent navigation in partially observable grid environments. This problem is challenging for centralized planning approaches as they,…
Forgetful Experience Replay in Hierarchical Reinforcement Learning from Demonstrations
Alexey Skrynnik, Aleksey Staroverov, Ermek Aitygulov +3
Currently, deep reinforcement learning (RL) shows impressive results in complex gaming and robotic environments. Often these results are achieved at the expense of huge computation…