NovelGym: A Flexible Ecosystem for Hybrid Planning and Learning Agents Designed for Open Worlds

Fuente: arXiv
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Bibliographic Details
Main Authors: Goel, Shivam, Wei, Yichen, Lymperopoulos, Panagiotis, Chura, Klara, Scheutz, Matthias, Sinapov, Jivko
Format: Preprint
Published: 2024
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author Goel, Shivam
Wei, Yichen
Lymperopoulos, Panagiotis
Chura, Klara
Scheutz, Matthias
Sinapov, Jivko
author_facet Goel, Shivam
Wei, Yichen
Lymperopoulos, Panagiotis
Chura, Klara
Scheutz, Matthias
Sinapov, Jivko
contents As AI agents leave the lab and venture into the real world as autonomous vehicles, delivery robots, and cooking robots, it is increasingly necessary to design and comprehensively evaluate algorithms that tackle the ``open-world''. To this end, we introduce NovelGym, a flexible and adaptable ecosystem designed to simulate gridworld environments, serving as a robust platform for benchmarking reinforcement learning (RL) and hybrid planning and learning agents in open-world contexts. The modular architecture of NovelGym facilitates rapid creation and modification of task environments, including multi-agent scenarios, with multiple environment transformations, thus providing a dynamic testbed for researchers to develop open-world AI agents.
format Preprint
id arxiv_https___arxiv_org_abs_2401_03546
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle NovelGym: A Flexible Ecosystem for Hybrid Planning and Learning Agents Designed for Open Worlds
Goel, Shivam
Wei, Yichen
Lymperopoulos, Panagiotis
Chura, Klara
Scheutz, Matthias
Sinapov, Jivko
Artificial Intelligence
As AI agents leave the lab and venture into the real world as autonomous vehicles, delivery robots, and cooking robots, it is increasingly necessary to design and comprehensively evaluate algorithms that tackle the ``open-world''. To this end, we introduce NovelGym, a flexible and adaptable ecosystem designed to simulate gridworld environments, serving as a robust platform for benchmarking reinforcement learning (RL) and hybrid planning and learning agents in open-world contexts. The modular architecture of NovelGym facilitates rapid creation and modification of task environments, including multi-agent scenarios, with multiple environment transformations, thus providing a dynamic testbed for researchers to develop open-world AI agents.
title NovelGym: A Flexible Ecosystem for Hybrid Planning and Learning Agents Designed for Open Worlds
topic Artificial Intelligence
url https://arxiv.org/abs/2401.03546