Research

The goal of my research is to discover the computational principles of human thinking by building intelligent machines that learn through interaction with complex simulated worlds. Within deep reinforcement learning, my work focuses on:

Key papers: PlaNet, Plan2Explore, APD

Progress

Scaling up world models: PlaNet (2018), Dreamer (2019), DreamerV2 (2020)
Conceptual foundations: DeepNeuro (2019), APD (2020), AgentEval (2021)
Unsupervised exploration: Plan2Explore (2020)
Skill discovery: MPH (2018), LSP (2020)
Temporal abstraction: CWVAE (2021)
Uncertainty estimation: NCP (2018), BayesLayers (2019)

Technical Talk

This 20 minute talk gives an overview of my research. It shows a general framework for designing unsupervised intelligent agents and our practical progress on scaling them up.

It is an invited talk I gave at the ICLR 2021 workshops on Self-Supervised Reinforcement Learning and Never-Ending Reinforcement Learning.

Podcast

Robin invited me to the TalkRL Podcast, where we talk about deep learning & neuroscience, PlaNet, Dreamer, world models, latent dynamics, curious agents, and more!