6 citations · 19 across the 8 of their papers we have counts for
5 papers · 1 filter
Multitask Adaptation by Retrospective Exploration with Learned World Models
Artem Zholus, Aleksandr I. Panov
Model-based reinforcement learning (MBRL) allows solving complex tasks in a sample-efficient manner. However, no information is reused between the tasks. In this work, we propose a…
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,…
Delta Schema Network in Model-based Reinforcement Learning
Andrey Gorodetskiy, Alexandra Shlychkova, Aleksandr I. Panov
This work is devoted to unresolved problems of Artificial General Intelligence - the inefficiency of transfer learning. One of the mechanisms that are used to solve this problem in…
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…