IGLU Gridworld: Simple and Fast Environment for Embodied Dialog Agents
Artem Zholus
Alexey Skrynnik
Shrestha Mohanty
Zoya Volovikova
Julia Kiseleva
Artur Szlam
Marc-Alexandre Côté
Aleksandr I. Panov

Abstract
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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