most citedImitating Human Behaviour with Diffusion Models

23 citations · 40 across the 4 of their papers we have counts for

collaborators

6 papers

cs.AI20231 cited

Adaptive Scaffolding in Block-Based Programming via Synthesizing New Tasks as Pop Quizzes

Ahana Ghosh, Sebastian Tschiatschek, Sam Devlin +1

Block-based programming environments are increasingly used to introduce computing concepts to beginners. However, novice students often struggle in these environments, given the co…

cs.AI202323 cited

Imitating Human Behaviour with Diffusion Models

Tim Pearce, Tabish Rashid, Anssi Kanervisto +8

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 be…

cs.HC202316 cited

Navigates Like Me: Understanding How People Evaluate Human-Like AI in Video Games

Stephanie Milani, Arthur Juliani, Ida Momennejad +7

We aim to understand how people assess human likeness in navigation produced by people and artificially intelligent (AI) agents in a video game. To this end, we propose a novel AI…

cs.LG20231 cited

Trust-Region-Free Policy Optimization for Stochastic Policies

Mingfei Sun, Benjamin Ellis, Anuj Mahajan +3

Trust Region Policy Optimization (TRPO) is an iterative method that simultaneously maximizes a surrogate objective and enforces a trust region constraint over consecutive policies…

cs.LG20232 cited

Contrastive Meta-Learning for Partially Observable Few-Shot Learning

Adam Jelley, Amos Storkey, Antreas Antoniou +1

Many contrastive and meta-learning approaches learn representations by identifying common features in multiple views. However, the formalism for these approaches generally assumes…

cs.LG2021

Deterministic and Discriminative Imitation (D2-Imitation): Revisiting Adversarial Imitation for Sample Efficiency

Mingfei Sun, Sam Devlin, Katja Hofmann +1

Sample efficiency is crucial for imitation learning methods to be applicable in real-world applications. Many studies improve sample efficiency by extending adversarial imitation t…