Xland minigrid American Samoa -Europe's Solar Ascent

ICML XLand-MiniGrid: Scalable Meta-Reinforcement Learning

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

XLand-MiniGrid: Scalable Meta-Reinforcement Learning

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

NeurIPS XLand-MiniGrid: Scalable Meta-Reinforcement Learning

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

xminigrid

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

探索无界：XLand-MiniGrid

XLand-MiniGrid是一个基于JAX构建的元强化学习框架,其设计目标是提供多样化的任务和规则系统,同时保持易于理解和修改的特点。它的核心亮点在于其兼容性、性能

XLand-MiniGrid: Scalable Meta-Reinforcement Learning

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

Paper page

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

[晓理紫]每日论文分享(有中文摘要,源码或项目地址)--强化学习、机器人等_xland-minigrid

文章浏览阅读813次,点赞16次,收藏13次。受XLand的多样性和深度以及MiniGrid的简单性和极简主义的启发,我们推出了XLand-MiniGrid,这是一套用于元强化学习研究的工具和网格世界环境。XLand-MiniGrid是用JAX编写的,它被设计成高度可扩展的,并且有可能在GPU或TPU加速器上运行,从而在有限的资源下实现大

[PDF] XLand-MiniGrid: Scalable Meta-Reinforcement

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

XLand-MiniGrid: Scalable Meta-Reinforcement Learning

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

XLand-MiniGrid: Scalable Meta-Reinforcement Learning

introduce XLand-MiniGrid, a library of grid world environments for meta-RL research. It does not compromise on task complexity in favour of affordability, democratizing large scale experimentation with limited resources. 2 XLand-MiniGrid We present an initial release of XLand-MiniGrid(v0.0.1), a suit of tools and grid world environments

XLand-MiniGrid：JAX中的元强化学习利器

XLand-MiniGrid 是一个专为元强化学习研究设计的工具套件,结合了 XLand 的多样性和深度,以及 MiniGrid 的简洁性和极简主义。该项目完全使用 JAX 从头开始构建,旨在

Releases · corl-team/xland-minigrid

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

XLand-MiniGrid: Scalable Meta-Reinforcement Learning

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

NeurIPS Poster XLand-MiniGrid: Scalable Meta-Reinforcement

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

XLand-100B: A Large-Scale Multi-Task Dataset for In-Context

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: Scalable Meta-Reinforcement Learning

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

Abstract

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

Roadmap to v1.0 · Issue #1 · corl-team/xland-minigrid

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

MiniGrid Documentation

@article {MinigridMiniworld23, author = {Maxime Chevalier-Boisvert and Bolun Dai and Mark Towers and Rodrigo de Lazcano and Lucas Willems and Salem Lahlou and Suman Pal and Pablo Samuel Castro and Jordan Terry}, title = {Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks}, journal = {CoRR}, volume =

Abstract arXiv:2312.12044v2 [cs.LG] 6 Feb 2024

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 learn-ing research. Written in JAX, XLand-MiniGrid is designed to be highly scalable and can poten-tially run on GPU or TPU accelerators, democ-

Российские ученые создали платформу для контекстного

Исследователи Университета Иннополис, T-Bank AI Research и AIRI разработали XLand-MiniGrid — открытую виртуальную среду для исследований в области контекстного обучения с подкреплением (In-Context RL).

XLand-MiniGrid：JAX 中的可扩展元强化学习环境,arXiv

我们推出 XLand-MiniGrid,这是一套工具和网格世界环境,适用于元强化学习研究的灵感来自于多样性和深度 XLand 和 MiniGrid 的简单性和极简主义。 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。

[2312.12044] XLand-MiniGrid: Scalable Meta-Reinforcement

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

xland-minigrid/README.md at main · corl-team/xland-minigrid

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

XLand-MiniGrid Walkthrough

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.

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