XLand-MiniGrid is a suite of tools, grid-world environments and benchmarks for meta-reinforcement learning research inspired bynthe diversity and depth of XLandnand the simplicity and minimalism of MiniGrid. Despite the similarities,nXLand-MiniGrid is written in JAX from scratch and designed to be highly scalable, democratizing large-scale
We present XLand-MiniGrid, a suite of tools and grid-world environments for meta-reinforcement learning research inspired by the diversity and depth of XLand and the simplicity and minimalism of MiniGrid. XLand-Minigrid is written in JAX, designed to be highly scalable, and can potentially run on GPU or TPU accelerators,
We''re unfortunately unlikely to be doing this anytime soon (it''s in the plans for post v1.0, ~2-3 months), as we''re currently busy working on getting XLand-MiniGrid to full paper and focused on meta-RL part (benchmarks), but we welcome any contributions, as grid randomization will definitely add new challenges to the meta-learning, as well as
Key (like in Minigrid) Door (like in Minigrid) Box (like in Minigrid) (may reduce FPS!!!) Actions. stochasticity (could be done with a wrapper) Rules & Goals. procedural generator (like in xland v2) pre-sampled benchmarks, 500-1M tasks; Map. different grid layouts (mazes, rooms, objects) Envs. porting majority of minigrid envs; full xland
Например, в XLand-MiniGrid собрано 100 млрд примеров действий искусственного интеллекта в 30 тыс. задач. Это позволяет использовать готовые датасеты для обучения, а не проводить его каждый раз с нуля.
XLand-MiniGrid is a suite of tools and grid-world environments for meta-reinforcement learning research designed to be highly scalable and can potentially run on GPU or TPU accelerators, democratizing large-scale experimentation with limited resources. Inspired by the diversity and depth of XLand and the simplicity and minimalism of MiniGrid, we present
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 to be highly scalable and can potentially run on GPU or TPU accelerators, democratizing large-scale
Среда XLand-MiniGrid - оригинальное решение в области контекстного обучения с подкреплением. Так называется
MiniGrid, we present XLand-MiniGrid, a suite of tools and grid-world environ-ments for meta-reinforcement learning research. Written in JAX, XLand-MiniGrid is designed to be highly scalable and can potentially run on GPU or TPU acceler-ators, democratizing large-scale experimentation with limited resources. Along
XLand-Minigrid编写 在 JAX 中,设计为高度可扩展,并且可以在 GPU 或 TPU 上运行 加速器,以有限的资源实现大规模实验的民主化。 为了展示我们库的通用性,我们实
Продукт XLand-MiniGrid, История, 2024 Анонс продукта. История 2024: Анонс продукта. 29 ноября 2024 года стало известно о том, что российские ученые из лаборатории T-Bank AI Research и Института AIRI в сотрудничестве со студентами МФТИ
XLand-Minigrid is written in JAX, designed to be highly scalable, and can potentially run on GPU or TPU accelerators, democratizing large-scale experimentation with limited resources. To demonstrate the generality of our library, we have implemented some well-known single-task environments as well as new meta-learning environments capable of
Minigrid contains simple and easily configurable grid world environments to conduct Reinforcement Learning research. This library was previously known as gym-minigrid. Toggle site navigation sidebar. MiniGrid Documentation. Farama Foundation Hide navigation sidebar. Hide table of contents sidebar
We present XLand-MiniGrid, a suite of tools and grid-world environments for meta-reinforcement learning research inspired by the diversity and depth of XLand and the simplicity and minimalism of MiniGrid. XLand-Minigrid is written in JAX, designed to be highly scalable, and can potentially run on GPU or TPU accelerators, democratizing large-scale
Written in JAX, XLand-MiniGrid is designed to be highly scalable and can potentially run on GPU or TPU accelerators, democratizing large-scale experimentation with limited resources. Along with the environments, XLand-MiniGrid provides pre-sampled benchmarks with millions of unique tasks of varying difficulty and easy-to-use baselines that
What''s Changed. This is our first stable release accompanied with the public full paper preprint on the arxiv (there is a lot of new content!). Compared to the workshop version, the library was almost completely rewritten, previously missing benchmarks, examples and baselines were added, and the interface of the environments was redesigned the latest update we added
Written in JAX, XLand-MiniGrid is designed to be highly scalable and can potentially run on GPU or TPU accelerators, democratizing large-scale experimentation with limited resources. Along with the environments, XLand-MiniGrid provides pre-sampled benchmarks with millions of unique tasks of varying difficulty and easy-to-use baselines that
Environment. XLand-MiniGrid is a complete rewrite of MiniGrid (Chevalier-Boisvert et al., 2023) in JAX (Bradbury et al., 2018), incorporating a notion of rules and goals from XLand (Team et al., 2023). Leveraging JAX, it can run on a GPU or TPU accelerators at millions steps per seconds. At its core, it is a goal-oriented
XLand-MiniGrid is a suite of tools, grid-world environments and benchmarks for meta-reinforcement learning research inspired by the diversity and depth of XLand and the simplicity and minimalism of MiniGrid. Despite the similarities, XLand-MiniGrid is written in JAX from scratch and designed to be highly scalable, democratizing large-scale
Виртуальная среда XLand-MiniGrid, в которой ИИ обучается принимать решения и выполнять новые действия, создана группой учёных из лаборатории научных исследований искусственного интеллекта T-Bank AI Research и Института AIRI при
Abstract: We present XLand-Minigrid, a suite of tools and grid-world environments for meta-reinforcement learning research inspired by the diversity and depth of XLand and the simplicity and minimalism of MiniGrid. XLand-Minigrid is written in JAX, designed to be highly scalable, and can potentially run on GPU or TPU accelerators, democratizing large-scale experimentation
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 to be highly scalable and can potentially run on GPU or TPU accelerators, democratizing large-scale
We present XLand-MiniGrid, a suite of tools and grid-world environments for meta-reinforcement learning research inspired by the diversity and depth of XLand and the simplicity and minimalism of MiniGrid. XLand-Minigrid is written in JAX, designed to be highly scalable, and can potentially run on GPU or TPU accelerators, democratizing large-scale experimentation with limited
In XLand-MiniGrid, the system of rules and goals is the cornerstone of the emergent complexity and diversity. In the original MiniGrid some environments have dynamic goals, but the dynamics are never changed. To train and evaluate highly adaptive agents, we need to be able to change the dynamics in non-trivial ways.
