Artem Zholus

I am a first year PhD student in MILA and Polytechnique Montréal supervised by Prof. Sarath Chandar. Besides, I am collaborating with Prof. Amir Zamir on a project that I started as a VILAB intern. My current research focuses on learning structure between actions and objects. My ultimate research goal is to build adaptive agents that can act under open-ended goals. For that, I leverage reinforcement learning, computer vision, and natural language processing.

Previously I had two internships at EPFL: at the LIONS lab under Prof. Volkan Cevher and at the VILAB under Prof. Amir Zamir. I obtained my Masters degree at MIPT studying AI, ML, and Cognitive Sciences and working at the CDS lab under Prof. Aleksandr Panov on task generalization in model-based reinforcement learning. I received my BSc degree from ITMO University majoring in Computer Science and Applied Mathematics.

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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
Embodied AI workshop @ CVPR, 2022
arxiv / code / slides /

A lightweight reinforcement learning environment for building embodied agents with language context tasked to build 3D structures in Minecraft-like world.

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IGLU 2022: Interactive Grounded Language Understanding in a Collaborative Environment at NeurIPS 2022

Julia Kiseleva*, Alexey Skrynni*, Artem Zholus*, Shrestha Mohanty*, Negar Arabzadeh*, Marc-Alexandre Côté*, Mohammad Aliannejadi, Milagro Teruel, Ziming Li, Mikhail Burtsev, Maartje ter Hoeve, Zoya Volovikova, Aleksandr Panov, Yuxuan Sun, Kavya Srinet, Arthur Szlam, Ahmed Awadallah
NeurIPS, Competition Track, 2022
website / arxiv / code /

AI competition where the goal is to follow a language instruction with context while being embodied in a 3D blocks world (RL track) and to ask a clarifying question in the case of ambiguity (NLP track).

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Factorized World Models for Learning Causal Relationships

Artem Zholus, Yaroslav Ivchenkov, and Aleksandr Panov
OSC workshop, ICLR, 2022
arxiv / code /

An RL agent that can generalize behavior on unseen tasks, which is done by learning a structured world model and constraining task specific information.

Design and source code from Jon Barron's website