Nicholas Waytowich

Towards Solving Fuzzy Tasks with Human Feedback: A Retrospective of the MineRL BASALT 2022 Competition

Mar 23, 2023
Stephanie Milani, Anssi Kanervisto, Karolis Ramanauskas, Sander Schulhoff, Brandon Houghton, Sharada Mohanty, Byron Galbraith, Ke Chen, Yan Song, Tianze Zhou, Bingquan Yu, He Liu, Kai Guan, Yujing Hu, Tangjie Lv, Federico Malato, Florian Leopold, Amogh Raut, Ville Hautamäki, Andrew Melnik, Shu Ishida, João F. Henriques, Robert Klassert, Walter Laurito, Ellen Novoseller, Vinicius G. Goecks, Nicholas Waytowich, David Watkins, Josh Miller, Rohin Shah

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Towards an Interpretable Hierarchical Agent Framework using Semantic Goals

Oct 16, 2022
Bharat Prakash, Nicholas Waytowich, Tim Oates, Tinoosh Mohsenin

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Multiple View Performers for Shape Completion

Sep 13, 2022
David Watkins-Valls, Peter Allen, Krzysztof Choromanski, Jacob Varley, Nicholas Waytowich

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* 6 pages, 2 pages of references, 6 figures, 3 tables

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Learning to Guide Multiple Heterogeneous Actors from a Single Human Demonstration via Automatic Curriculum Learning in StarCraft II

May 11, 2022
Nicholas Waytowich, James Hare, Vinicius G. Goecks, Mark Mittrick, John Richardson, Anjon Basak, Derrik E. Asher

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* Submitted to the 2022 SPIE Defense + Commercial Sensing (DCS) Conference on "Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV"

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Retrospective on the 2021 BASALT Competition on Learning from Human Feedback

Apr 14, 2022
Rohin Shah, Steven H. Wang, Cody Wild, Stephanie Milani, Anssi Kanervisto, Vinicius G. Goecks, Nicholas Waytowich, David Watkins-Valls, Bharat Prakash, Edmund Mills, Divyansh Garg, Alexander Fries, Alexandra Souly, Chan Jun Shern, Daniel del Castillo, Tom Lieberum

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* Accepted to the PMLR NeurIPS 2021 Demo & Competition Track volume

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Combining Learning from Human Feedback and Knowledge Engineering to Solve Hierarchical Tasks in Minecraft

Dec 07, 2021
Vinicius G. Goecks, Nicholas Waytowich, David Watkins, Bharat Prakash

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* Submitted to the AAAI 2022 Spring Symposium on Machine Learning and Knowledge Engineering for Hybrid Intelligence (AAAI-MAKE 2022)

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