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Computer Science > Artificial Intelligence

arXiv:2301.10677 (cs)
[Submitted on 25 Jan 2023]

Title:Imitating Human Behaviour with Diffusion Models

Authors:Tim Pearce, Tabish Rashid, Anssi Kanervisto, Dave Bignell,
Mingfei Sun, Raluca Georgescu, Sergio Valcarcel Macua, Shan Zheng Tan
, Ida Momennejad, Katja Hofmann, Sam Devlin
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    Abstract: Diffusion models have emerged as powerful generative
    models in the text-to-image domain. This paper studies their
    application as observation-to-action models for imitating human
    behaviour in sequential environments. Human behaviour is
    stochastic and multimodal, with structured correlations between
    action dimensions. Meanwhile, standard modelling choices in
    behaviour cloning are limited in their expressiveness and may
    introduce bias into the cloned policy. We begin by pointing out
    the limitations of these choices. We then propose that diffusion
    models are an excellent fit for imitating human behaviour, since
    they learn an expressive distribution over the joint action
    space. We introduce several innovations to make diffusion models
    suitable for sequential environments; designing suitable
    architectures, investigating the role of guidance, and developing
    reliable sampling strategies. Experimentally, diffusion models
    closely match human demonstrations in a simulated robotic control
    task and a modern 3D gaming environment.

Comments:          Published in ICLR 2023
Subjects:          Artificial Intelligence (cs.AI); Machine Learning
                   (cs.LG); Machine Learning (stat.ML)
Cite as:           arXiv:2301.10677 [cs.AI]
                   (or arXiv:2301.10677v1 [cs.AI] for this version)
                   https://doi.org/10.48550/arXiv.2301.10677
                   Focus to learn more
                   arXiv-issued DOI via DataCite
Journal reference: ICLR 2023

Submission history

From: Tim Pearce [view email]
[v1] Wed, 25 Jan 2023 16:31:05 UTC (10,605 KB)
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