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Imitating Human Behaviour with Diffusion Models
Tim Pearce · Tabish Rashid · Anssi Kanervisto · David Bignell · Mingfei Sun · Raluca Georgescu · Sergio Valcarcel Macua · Shan Zheng Tan · Ida Momennejad · Katja Hofmann · Sam Devlin
Event URL: https://openreview.net/forum?id=-pqCZ8tbtd »

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.

Author Information

Tim Pearce (Microsoft Research)
Tabish Rashid (University of Oxford)
Anssi Kanervisto (Microsoft Research)
David Bignell (Research, Microsoft)
Mingfei Sun (Microsoft Research)
Raluca Georgescu (Microsoft)
Sergio Valcarcel Macua (Microsoft Research)
Shan Zheng Tan (Research, Microsoft)
Shan Zheng Tan


Ida Momennejad (Microsoft Research)
Katja Hofmann (Microsoft Research)

Dr. Katja Hofmann is a Principal Researcher at the [Game Intelligence](http://aka.ms/gameintelligence/) group at [Microsoft Research Cambridge, UK](https://www.microsoft.com/en-us/research/lab/microsoft-research-cambridge/). There, she leads a research team that focuses on reinforcement learning with applications in modern video games. She and her team strongly believe that modern video games will drive a transformation of how we interact with AI technology. One of the projects developed by her team is [Project Malmo](https://www.microsoft.com/en-us/research/project/project-malmo/), which uses the popular game Minecraft as an experimentation platform for developing intelligent technology. Katja's long-term goal is to develop AI systems that learn to collaborate with people, to empower their users and help solve complex real-world problems. Before joining Microsoft Research, Katja completed her PhD in Computer Science as part of the [ILPS](https://ilps.science.uva.nl/) group at the [University of Amsterdam](https://www.uva.nl/en). She worked with Maarten de Rijke and Shimon Whiteson on interactive machine learning algorithms for search engines.

Sam Devlin (Microsoft Research)

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