Stephanie Milani

I am a Ph.D. student in the Machine Learning Department at Carnegie Mellon University, where I am advised by Fei Fang. Previously, I interned at Microsoft Research Montreal with Geoff Gordon and in the Microsoft Research Cambridge Deep Reinforcement Learning for Games group with Katja Hofmann. I aim to create intelligent agents that can learn quickly, explain their decisions, and work harmoniously with people and other artificially intelligent agents. I am particularly interested in reinforcement learning.

I completed my B.S. in Computer Science and B.A. in Psychology at the University of Maryland, Baltimore County, where I worked with Marie desJardins and Cynthia Matuszek.

I am open to and excited about collaborating with others. Please email me if you have any questions about a machine learning (or psychology) project, or if you are interested in a research collaboration.

Email  /  CV  /  Google Scholar  /  LinkedIn  /  Twitter

Selected Publications

MABL: Bi-Level Latent-Variable World Model for Sample-Efficient Multi-Agent Reinforcement Learning
Aravind Venugopal, Stephanie Milani, Fei Fang, Balaraman Ravindran
AAMAS, 2024

Interpretable Multi-Agent Reinforcement Learning with Decision-Tree Policies
Stephanie Milani, Zhicheng Zhang, Nicholay Topin, Zheyuan Ryan Shi, Charles Kamhoua, Evangelos E. Papalexakis, Fei Fang
Explainable Agency in Artificial Intelligence, 2024 (Invited)

BEDD: The MineRL BASALT Evaluation and Demonstrations Dataset for Training and Benchmarking Agents that Solve Fuzzy Tasks
Stephanie Milani, Anssi Kanervisto, Karolis Ramanauskas, Sander Schulhoff, Brandon Houghton, Rohin Shah
NeurIPS Datasets & Benchmarks Track, 2023 (Oral)

Explainable Reinforcement Learning: A Survey and Comparative Review
Stephanie Milani, Nicholay Topin, Manuela Veloso, Fei Fang
ACM CSUR Special Issue on Trustworthy AI, 2023

Navigates Like Me: Understanding How People Evaluate Human-Like AI in Video Games
Stephanie Milani, Arthur Juliani, Ida Momennejad, Raluca Georgescu, Jaroslaw Rzpecki, Alison Shaw, Gavin Costello, Fei Fang, Sam Devlin, Katja Hofmann
CHI, 2023
Previous versions in NeurIPS-21 Workshop on Human-Centered AI and CHI-22 Late Breaking Work

Uni[MASK]: Unified Inference in Sequential Decision Problems
Micah Carroll, Jessy Lin, Orr Paradise, Raluca Georgescu, Mingfei Sun, David Bignell, Stephanie Milani, Katja Hofmann, Matthew Hausknecht, Anca Dragan, Sam Devlin
NeurIPS, 2022 (Oral)
Previous version in ICLR-22 Workshop on Generalizable Policy Learning in the Physical World

MAVIPER: Learning Decision Tree Policies for Interpretable Multi-Agent Reinforcement Learning
Stephanie Milani*, Zhicheng Zhang*, Nicholay Topin, Zheyuan Ryan Shi, Charles Kamhoua, Evangelos E. Papalexakis, Fei Fang
Previous version in AAAI-22 Explainable Agency in AI Workshop

Learning to Play Adaptive Cyber Deception Game
Yinuo Du, Zimeng Song, Stephanie Milani, Coty Gonzalez, Fei Fang
AAMAS OptLearnMAS Workshop, 2022

Iterative Bounding MDPs: Learning Interpretable Policies via Non-Interpretable Methods
Nicholay Topin, Stephanie Milani, Fei Fang, Manuela Veloso
AAAI, 2021

Harnessing the Power of Deception in Attack Graph-Based Security Games
Stephanie Milani, Weiran Shen, Kevin S. Chan, Sridhar Venkatesan, Nandi O. Leslie, Charles Kamhoua, Fei Fang
GameSec, 2020

Planning with Abstract, Learned Models While Learning Transferable Subtasks
John Winder, Stephanie Milani, Matthew Landen, Erebus Oh, Shane Parr, Shawn Squire, Marie desJardins, Cynthia Matuszek
AAAI, 2020
Previous versions in ICAPS-17 IntEx Workshop, RLDM-17, and Do Good Robotics Symposium 2019

Intelligent Tutoring Strategies for Students with Autism Spectrum Disorder: A Reinforcement Learning Approach
Stephanie Milani*, Zhou Fan*, Saurabh Gulati, Thanh Nguyen, Fei Fang, Amulya Yadav
AAAI Workshop on AI for Education, 2020

Guaranteeing Reproducibility in Deep Learning Competitions
Brandon Houghton, Stephanie Milani, Nicholay Topin, William H. Guss, Katja Hofmann, Diego Perez-Liebana, Manuela Veloso, Ruslan Salakhutdinov
NeurIPS CiML Workshop, 2019

Penalty-Modified Markov Decision Processes: Efficient Incorporation of Norms into Sequential Decision Making Problems
Stephanie Milani, Nicholay Topin, Katia Sycara
RLDM, 2019

Perceptions of Domestic Robots' Normative Behavior Across Cultures
Huao Li, Stephanie Milani, Vigneshram Krishnamoorthy, Michael Lewis, Katia Sycara
AIES, 2019

Selected Competition Papers

The MineRL BASALT Competition on Fine-tuning from Human Feedback
Anssi Kanervisto, Stephanie Milani, Karolis Ramanauskas, Byron V. Galbraith, Steven H. Wang, Brandon Houghton, Sharada Mohanty, Rohin Shah
NeurIPS Competition Track, 2022

The MineRL Diamond Competition on Sample Efficient Reinforcement Learning
William H. Guss, Alara Dirik*, Byron Galbraith*, Brandon Houghton*, Anssi Kanervisto*, Noboru Sean Kuno, Stephanie Milani*, Sharada Mohanty*, Karolis Ramanauskas*, Ruslan Salakhutdinov*, Rohin Shah*, Nicholay Topin*, Steven H. Wang*, Cody Wild*
NeurIPS Competition Track, 2021

The MineRL BASALT Competition on Learning from Human Feedback
Rohin Shah, Cody Wild, Steven H. Wang, Neel Alex, Brandon Houghton, William H. Guss, Stephanie Milani, Nicholay Topin, Pieter Abbeel, Stuart Russell, Anca Dragan
NeurIPS Competition Track, 2021

NeurIPS 2020 Competition: The MineRL Competition on Sample Efficient Reinforcement Learning using Human Priors
William H. Guss, Mario Ynocente Castro*, Sam Devlin*, Brandon Houghton*, Noboru Sean Kuno*, Crissman Loomis*, Stephanie Milani*, Sharada Mohanty*, Keisuke Nakata*, Ruslan Salakhutdinov*, John Schulman, Shinya Shiroshita*, Nicholay Topin*, Avinash Ummadisingu*, Oriol Vinyals*
NeurIPS Competition Track, 2020

The MineRL Competition on Sample Efficient Reinforcement Learning using Human Priors
William H. Guss, Cayden Codel*, Katja Hofmann*, Brandon Houghton*, Noboru Sean Kuno*, Stephanie Milani*, Sharada Mohanty*, Diego Perez-Liebana*, Ruslan Salakhutdinov*, Nicholay Topin*, Manuela Veloso*, Phillip Wang*
NeurIPS Competition Track, 2019

I got this great website here.