2 citations · 3 across the 13 of their papers we have counts for
9 papers · 1 filter
Vintix II: Decision Pre-Trained Transformer is a Scalable In-Context Reinforcement Learner
Andrei Polubarov, Lyubaykin Nikita, Alexander Derevyagin +11
Recent progress in in-context reinforcement learning (ICRL) has demonstrated its potential for training generalist agents that can acquire new tasks directly at inference. Algorith…
Zero-Shot Adaptation of Behavioral Foundation Models to Unseen Dynamics
Maksim Bobrin, Ilya Zisman, Alexander Nikulin +2
Behavioral Foundation Models (BFMs) proved successful in producing policies for arbitrary tasks in a zero-shot manner, requiring no test-time training or task-specific fine-tuning.…
Yes, Q-learning Helps Offline In-Context RL
Denis Tarasov, Alexander Nikulin, Ilya Zisman +6
Existing offline in-context reinforcement learning (ICRL) methods have predominantly relied on supervised training objectives, which are known to have limitations in offline RL set…
Vintix: Action Model via In-Context Reinforcement Learning
Andrey Polubarov, Nikita Lyubaykin, Alexander Derevyagin +4
In-Context Reinforcement Learning (ICRL) represents a promising paradigm for developing generalist agents that learn at inference time through trial-and-error interactions, analogo…
N-Gram Induction Heads for In-Context RL: Improving Stability and Reducing Data Needs
Ilya Zisman, Alexander Nikulin, Viacheslav Sinii +5
In-context learning allows models like transformers to adapt to new tasks from a few examples without updating their weights, a desirable trait for reinforcement learning (RL). How…
XLand-100B: A Large-Scale Multi-Task Dataset for In-Context Reinforcement Learning
Alexander Nikulin, Ilya Zisman, Alexey Zemtsov +1
Following the success of the in-context learning paradigm in large-scale language and computer vision models, the recently emerging field of in-context reinforcement learning is ex…
In-Context Reinforcement Learning for Variable Action Spaces
Viacheslav Sinii, Alexander Nikulin, Vladislav Kurenkov +2
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. A key limit…
Emergence of In-Context Reinforcement Learning from Noise Distillation
Ilya Zisman, Vladislav Kurenkov, Alexander Nikulin +2
Recently, extensive studies in Reinforcement Learning have been carried out on the ability of transformers to adapt in-context to various environments and tasks. Current in-context…
XLand-MiniGrid: Scalable Meta-Reinforcement Learning Environments in JAX
Alexander Nikulin, Vladislav Kurenkov, Ilya Zisman +3
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-reinfor…