1 code implementation • 22 Dec 2023 • Allen Chang, Matthew C. Fontaine, Serena Booth, Maja J. Matarić, Stefanos Nikolaidis
QDGS is a model-agnostic framework that uses prompt guidance to optimize a quality objective across measures of diversity for synthetically generated data, without fine-tuning the generative model.
no code implementations • 18 Dec 2023 • David H. Lee, Anishalakshmi V. Palaparthi, Matthew C. Fontaine, Bryon Tjanaka, Stefanos Nikolaidis
We propose Density Descent Search (DDS), an algorithm that explores the feature space via gradient descent on a continuous density estimate of the feature space that also provides stronger stability guarantee.
1 code implementation • NeurIPS 2023 • Yulun Zhang, Matthew C. Fontaine, Varun Bhatt, Stefanos Nikolaidis, Jiaoyang Li
We show that NCA environment generators maintain consistent, regularized patterns regardless of environment size, significantly enhancing the scalability of multi-robot systems in two different domains with up to 2, 350 robots.
no code implementations • 23 May 2023 • Sumeet Batra, Bryon Tjanaka, Matthew C. Fontaine, Aleksei Petrenko, Stefanos Nikolaidis, Gaurav Sukhatme
Training generally capable agents that thoroughly explore their environment and learn new and diverse skills is a long-term goal of robot learning.
1 code implementation • 10 May 2023 • Yulun Zhang, Matthew C. Fontaine, Varun Bhatt, Stefanos Nikolaidis, Jiaoyang Li
We show that, even with state-of-the-art MAPF algorithms, commonly used human-designed layouts can lead to congestion for warehouses with large numbers of robots and thus have limited scalability.
1 code implementation • 26 Apr 2023 • Varun Bhatt, Heramb Nemlekar, Matthew C. Fontaine, Bryon Tjanaka, Hejia Zhang, Ya-Chuan Hsu, Stefanos Nikolaidis
In the shared control teleoperation domain and a more complex shared workspace collaboration task, we show that surrogate assisted scenario generation efficiently synthesizes diverse datasets of challenging scenarios.
1 code implementation • 1 Mar 2023 • Bryon Tjanaka, Matthew C. Fontaine, David H. Lee, Yulun Zhang, Nivedit Reddy Balam, Nathaniel Dennler, Sujay S. Garlanka, Nikitas Dimitri Klapsis, Stefanos Nikolaidis
Recent years have seen a rise in the popularity of quality diversity (QD) optimization, a branch of optimization that seeks to find a collection of diverse, high-performing solutions to a given problem.
1 code implementation • 6 Oct 2022 • Bryon Tjanaka, Matthew C. Fontaine, David H. Lee, Aniruddha Kalkar, Stefanos Nikolaidis
Pre-training a diverse set of neural network controllers in simulation has enabled robots to adapt online to damage in robot locomotion tasks.
no code implementations • 20 Jun 2022 • Ya-Chuan Hsu, Matthew C. Fontaine, Sam Earle, Maria Edwards, Julian Togelius, Stefanos Nikolaidis
To target specific diversity in the arrangements, we optimize the latent space of the GAN via a quality diversity algorithm to generate a diverse arrangement collection.
no code implementations • 9 Jun 2022 • Varun Bhatt, Bryon Tjanaka, Matthew C. Fontaine, Stefanos Nikolaidis
Results in two benchmark domains show that DSAGE significantly outperforms existing QD environment generation algorithms in discovering collections of environments that elicit diverse behaviors of a state-of-the-art RL agent and a planning agent.
1 code implementation • 22 May 2022 • Matthew C. Fontaine, Stefanos Nikolaidis
Quality diversity (QD) optimization algorithms, such as Covariance Matrix Adaptation MAP-Elites (CMA-ME), search for a collection of solutions that are both high-quality with respect to an objective and diverse with respect to specified measure functions.
1 code implementation • 8 Feb 2022 • Bryon Tjanaka, Matthew C. Fontaine, Julian Togelius, Stefanos Nikolaidis
Training can then be viewed as a quality diversity (QD) optimization problem, where we search for a collection of performant policies that are diverse with respect to quantified behavior.
1 code implementation • 7 Dec 2021 • Yulun Zhang, Matthew C. Fontaine, Amy K. Hoover, Stefanos Nikolaidis
In a Hearthstone deckbuilding case study, we show that our approach improves the sample efficiency of MAP-Elites and outperforms a model trained offline with random decks, as well as a linear surrogate model baseline, setting a new state-of-the-art for quality diversity approaches in automated Hearthstone deckbuilding.
2 code implementations • 12 Sep 2021 • Sam Earle, Justin Snider, Matthew C. Fontaine, Stefanos Nikolaidis, Julian Togelius
We present a method of generating diverse collections of neural cellular automata (NCA) to design video game levels.
1 code implementation • 21 Jun 2021 • Matthew C. Fontaine, Ya-Chuan Hsu, Yulun Zhang, Bryon Tjanaka, Stefanos Nikolaidis
When studying robots collaborating with humans, much of the focus has been on robot policies that coordinate fluently with human teammates in collaborative tasks.
2 code implementations • NeurIPS 2021 • 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 function but are also diverse with respect to a set of specified measure functions.
1 code implementation • 13 Oct 2020 • Hejia Zhang, Matthew C. Fontaine, Amy K. Hoover, Julian Togelius, Bistra Dilkina, Stefanos Nikolaidis
Recent advancements in procedural content generation via machine learning enable the generation of video-game levels that are aesthetically similar to human-authored examples.
1 code implementation • 11 Jul 2020 • Matthew C. Fontaine, Ruilin Liu, Ahmed Khalifa, Jignesh Modi, Julian Togelius, Amy K. Hoover, Stefanos Nikolaidis
Generative adversarial networks (GANs) are quickly becoming a ubiquitous approach to procedurally generating video game levels.
6 code implementations • 5 Dec 2019 • Matthew C. Fontaine, Julian Togelius, Stefanos Nikolaidis, Amy K. Hoover
Results from experiments based on standard continuous optimization benchmarks show that CMA-ME finds better-quality solutions than MAP-Elites; similarly, results on the strategic game Hearthstone show that CMA-ME finds both a higher overall quality and broader diversity of strategies than both CMA-ES and MAP-Elites.
no code implementations • 2 Jul 2019 • Fernando de Mesentier Silva, Rodrigo Canaan, Scott Lee, Matthew C. Fontaine, Julian Togelius, Amy K. Hoover
Balancing an ever growing strategic game of high complexity, such as Hearthstone is a complex task.
1 code implementation • 24 Apr 2019 • Matthew C. Fontaine, Scott Lee, L. B. Soros, Fernando De Mesentier Silva, Julian Togelius, Amy K. Hoover
Quality diversity (QD) algorithms such as MAP-Elites have emerged as a powerful alternative to traditional single-objective optimization methods.