Research

I am interested in building adaptive agents that can safely navigate complex environments to achieve desired outcomes. To this end, I am focused on the framework of Bayesian model-based reinforcement learning. I believe it provides the natural basis for building robust and adaptive artificial agents that can deal with stochastic and partially-observable environments.

More specifically, my research has been dedicated to creating better world models (latent-variable generative modeling) and model-based agents with Bayesian planning objectives.

Publications

Long-horizon video prediction

Long-horizon video prediction using a dynamic latent hierarchy

A Zakharov, Q Guo, Z Fountas

Preprint, 2022

Non-reinforced preferences

Modelling non-reinforced preferences using selective attention

N Sajid, P Tigas, Z Fountas, Q Guo, A Zakharov, L Da Costa

NeurIPS 2022, WiML

Bayesian sense of time

Bayesian sense of time in biological and artificial brains

Z Fountas*, A Zakharov* (*equal contribution)

Book chapter in Time and Science 2022, World Scientific Publishing

Exploration and preference satisfaction

Exploration and preference satisfaction trade-off in reward-free learning

N Sajid, P Tigas, A Zakharov, Z Fountas, K Friston

ICML 2021, WURL

Episodic memory

Episodic memory for subjective-timescale models

A Zakharov, M Crosby, Z Fountas

ICML 2021, WURL

Geometric Deep Learning

Geometric Deep Learning for Post-Menstrual Age Prediction

V Vosylius, A Wang, C Waters, A Zakharov, et al.

International Workshop on Graphs in Biomedical Image Analysis 2020