IGLU Gridworld: Simple and Fast Environment for Embodied Dialog Agents

Autor:	Zholus, Artem, Skrynnik, Alexey, Mohanty, Shrestha, Volovikova, Zoya, Kiseleva, Julia, Szlam, Artur, Coté, Marc-Alexandre, Panov, Aleksandr I.
Rok vydání:	2022
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	We present the IGLU Gridworld: a reinforcement learning environment for building and evaluating language conditioned embodied agents in a scalable way. The environment features visual agent embodiment, interactive learning through collaboration, language conditioned RL, and combinatorically hard task (3d blocks building) space.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2206.00142 Zobrazit plný text záznamu View this record from Arxiv