IGLU Gridworld: Simple and Fast Environment for Embodied Dialog Agents

Artem Zholus,Alexey Skrynnik,Shrestha Mohanty,Zoya Volovikova,Julia Kiseleva, Artur Szlam,Marc-Alexandre Coté,Aleksandr I. Panov

arxiv（2022）

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摘要

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.

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