1 code implementation • 20 Dec 2023 • Viacheslav Sinii, Alexander Nikulin, Vladislav Kurenkov, Ilya Zisman, Sergey Kolesnikov
Recently, it has been shown that transformers pre-trained on diverse datasets with multi-episode contexts can generalize to new reinforcement learning tasks in-context.
1 code implementation • 19 Dec 2023 • Ilya Zisman, Vladislav Kurenkov, Alexander Nikulin, Viacheslav Sinii, Sergey Kolesnikov
Recently, extensive studies in Reinforcement Learning have been carried out on the ability of transformers to adapt in-context to various environments and tasks.
1 code implementation • 19 Dec 2023 • Alexander Nikulin, Vladislav Kurenkov, Ilya Zisman, Artem Agarkov, Viacheslav Sinii, Sergey Kolesnikov
Inspired by the diversity and depth of XLand and the simplicity and minimalism of MiniGrid, we present XLand-MiniGrid, a suite of tools and grid-world environments for meta-reinforcement learning research.
1 code implementation • NeurIPS 2023 • Vladislav Kurenkov, Alexander Nikulin, Denis Tarasov, Sergey Kolesnikov
NetHack is known as the frontier of reinforcement learning research where learning-based methods still need to catch up to rule-based solutions.
1 code implementation • NeurIPS 2023 • Denis Tarasov, Vladislav Kurenkov, Alexander Nikulin, Sergey Kolesnikov
Recent years have witnessed significant advancements in offline reinforcement learning (RL), resulting in the development of numerous algorithms with varying degrees of complexity.
3 code implementations • 31 Jan 2023 • Alexander Nikulin, Vladislav Kurenkov, Denis Tarasov, Sergey Kolesnikov
Despite the success of Random Network Distillation (RND) in various domains, it was shown as not discriminative enough to be used as an uncertainty estimator for penalizing out-of-distribution actions in offline reinforcement learning.
2 code implementations • 20 Nov 2022 • Dmitriy Akimov, Vladislav Kurenkov, Alexander Nikulin, Denis Tarasov, Sergey Kolesnikov
This Normalizing Flows action encoder is pre-trained in a supervised manner on the offline dataset, and then an additional policy model - controller in the latent space - is trained via reinforcement learning.
2 code implementations • 20 Nov 2022 • Alexander Nikulin, Vladislav Kurenkov, Denis Tarasov, Dmitry Akimov, Sergey Kolesnikov
Training large neural networks is known to be time-consuming, with the learning duration taking days or even weeks.
3 code implementations • NeurIPS 2023 • Denis Tarasov, Alexander Nikulin, Dmitry Akimov, Vladislav Kurenkov, Sergey Kolesnikov
CORL is an open-source library that provides thoroughly benchmarked single-file implementations of both deep offline and offline-to-online reinforcement learning algorithms.
no code implementations • 8 Oct 2021 • Vladislav Kurenkov, Sergey Kolesnikov
In this work, we argue for the importance of an online evaluation budget for a reliable comparison of deep offline RL algorithms.
1 code implementation • 1 Oct 2021 • Vladislav Kurenkov, Bulat Maksudov
In recent years, Evolutionary Strategies were actively explored in robotic tasks for policy search as they provide a simpler alternative to reinforcement learning algorithms.
1 code implementation • 6 Apr 2020 • Vladislav Kurenkov, Hany Hamed, Sergei Savin
In this paper, we consider tensegrity hopper - a novel tensegrity-based robot, capable of moving by hopping.
1 code implementation • 27 Oct 2019 • Vladislav Kurenkov, Bulat Maksudov, Adil Khan
In this work, we analyze the performance of general deep reinforcement learning algorithms for a task-oriented language grounding problem, where language input contains multiple sub-goals and their order of execution is non-linear.