IGLU Gridworld: Simple and Fast Environment for Embodied Dialog Agents

Artem Zholus, Alexey Skrynnik, Shrestha Mohanty, Zoya Volovikova, Julia Kiseleva, Arthur 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.