30 citations · 40 across the 4 of their papers we have counts for
8 papers
Approximating Gradients for Differentiable Quality Diversity in Reinforcement Learning
Bryon Tjanaka, Matthew C. Fontaine, Julian Togelius +1
Consider the problem of training robustly capable agents. One approach is to generate a diverse collection of agent polices. Training can then be viewed as a quality diversity (QD)…
On the Importance of Environments in Human-Robot Coordination
Matthew C. Fontaine, Ya-Chuan Hsu, Yulun Zhang +2
When studying robots collaborating with humans, much of the focus has been on robot policies that coordinate fluently with human teammates in collaborative tasks. However, less emp…
Differentiable Quality Diversity
Matthew C. Fontaine, Stefanos Nikolaidis
Quality diversity (QD) is a growing branch of stochastic optimization research that studies the problem of generating an archive of solutions that maximize a given objective functi…
A Quality Diversity Approach to Automatically Generating Human-Robot Interaction Scenarios in Shared Autonomy
Matthew Fontaine, Stefanos Nikolaidis
The growth of scale and complexity of interactions between humans and robots highlights the need for new computational methods to automatically evaluate novel algorithms and applic…
Video Game Level Repair via Mixed Integer Linear Programming
Hejia Zhang, Matthew C. Fontaine, Amy K. Hoover +3
Recent advancements in procedural content generation via machine learning enable the generation of video-game levels that are aesthetically similar to human-authored examples. Howe…
Covariance Matrix Adaptation for the Rapid Illumination of Behavior Space
Matthew C. Fontaine, Julian Togelius, Stefanos Nikolaidis +1
We focus on the challenge of finding a diverse collection of quality solutions on complex continuous domains. While quality diver-sity (QD) algorithms like Novelty Search with Loca…