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pro vyhledávání: '"Zisman, Ilya"'
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). However, existing in-context RL methods, such as Algorithm Distillation (
Externí odkaz:
http://arxiv.org/abs/2411.01958
Autor:
Nikulin, Alexander, Zisman, Ilya, Zemtsov, Alexey, Sinii, Viacheslav, Kurenkov, Vladislav, Kolesnikov, Sergey
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 experiencing a rapid growth. However, its development has been held back
Externí odkaz:
http://arxiv.org/abs/2406.08973
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 limitation of previously proposed models is their reliance on a predefined
Externí odkaz:
http://arxiv.org/abs/2312.13327
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 RL methods are limited by their strict requirements for data, which n
Externí odkaz:
http://arxiv.org/abs/2312.12275
Autor:
Nikulin, Alexander, Kurenkov, Vladislav, Zisman, Ilya, Agarkov, Artem, Sinii, Viacheslav, Kolesnikov, Sergey
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. Written in JAX, XLand-MiniGrid is designed t
Externí odkaz:
http://arxiv.org/abs/2312.12044
Publikováno v:
AAMAS '23, Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems (May 2023) Pages 49-57
The majority of Multi-Agent Reinforcement Learning (MARL) literature equates the cooperation of self-interested agents in mixed environments to the problem of social welfare maximization, allowing agents to arbitrarily share rewards and private infor
Externí odkaz:
http://arxiv.org/abs/2306.08419