Ryan Boldi

I am currently an undergraduate at the Univeristy of Massachusetts Amherst. Recently, I have been working with Lee Spector on Lexicase selection, a novel selection strategy for evolutionary computation. Additionally, I have been working with Scott Niekum on improving alignment of reward functions learned from human preferences for Reinforcement Learning from human feedback (RLHF). Furthermore, I am working with Stefanos Nikolaidis on Quality Diversity Optimization for Reinforcement Learning.

My research interests include Neuroevolution, Genetic Programming, Cognitive Science, Reinforcement Learning and Robotics. My goal is to study the emergence of human intelligence from the perspective of evolving and learning autonomous systems. I am planning on applying to PhD programs for the Fall 2025 cycle.

News

Jun 3, 2024	Exicted to be at Carnegie Mellon University’s Robotics Institute this summer working with Dr. Katia Sycara on emergent communication between diverse agents in multi-agent reinforcement learning settings.
Mar 29, 2024	Happy to announce that I was selected as a 2024 Goldwater Scholar! I am grateful for the support of my mentors, friends, and family. This year, 438 scholarships were awarded to undergrads in the US, with only 30 going to students in the field of Computer Science.
Mar 10, 2024	3 short papers accepted to GECCO 2024! Amongst them is some work on integrating Quality Diversity Optimization with Reinforcement Learning, and an extension of our work on the interaction between selection and down-sampling training sets. I am excited to present this work in Melbourne, Australia this July!
Oct 28, 2023	Our work on solving deceptive domains without explicitely maintaining diversity was accepted to the NeurIPS 2023 Workshop on Agent Learning in Open-Endedness (ALOE).
May 2, 2023	Our paper on fairly comparing quality diveristy and objective based search algorithms was accepted to GECCO 2023’s QD Benchmarking Workshop!

Representative Papers

Pareto-Optimal Learning from Preferences with Hidden Context

Ryan Boldi, Li Ding, Lee Spector, and Scott Niekum

arXiv preprint arXiv:2406.15599 2024

arXiv

Informed Down-Sampled Lexicase Selection: Identifying productive training cases for efficient problem solving

Ryan Boldi*, Martin Briesch*, Dominik Sobania, Alexander Lalejini, and 4 more authors

Evolutionary Computation (MIT Press) 2024

arXiv Bib Supp Code

@article{boldi2024informed,
  title = {Informed Down-Sampled Lexicase Selection: Identifying productive training cases for efficient problem solving},
  author = {Boldi*, Ryan and Briesch*, Martin and Sobania, Dominik and Lalejini, Alexander and Helmuth, Thomas and Rothlauf, Franz and Ofria, Charles and Spector, Lee},
  journal = {Evolutionary Computation (MIT Press)},
  pages = {1--32},
  year = {2024},
  code = {https://github.com/ryanboldi/Informed-Down-Sampled-Lexicase/tree/main/propeller},
  supp = {https://ryanboldi.github.io/Informed-Down-Sampled-Lexicase/},
  arxiv = {2301.01488},
  preview = {IDS.png},
  bibtex_show = {true},
  selected = {true},
  publisher = {MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info~…}
}

Objectives Are All You Need: Solving Deceptive Problems Without Explicit Diversity Maintenance

Ryan Boldi, Li Ding, and Lee Spector

In The Workshop on Agent Learning in Open-Endedness (ALOE) at the Conference on Neural Information Processing Systems (NeurIPS) 2023

arXiv
Particularity

Lee Spector, Li Ding, and Ryan Boldi

In Genetic Programming Theory and Practice XX 2023

PDF

News

Representative Papers

See the publications tab or my Google Scholar for a full list.