544 citations · 2.2k across the 29 of their papers we have counts for
6 papers · 1 filter
Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data
Felipe Petroski Such, Aditya Rawal, Joel Lehman +2
This paper investigates the intriguing question of whether we can create learning algorithms that automatically generate training data, learning environments, and curricula in orde…
An Overview of Open-Ended Evolution: Editorial Introduction to the Open-Ended Evolution II Special Issue
Norman Packard, Mark A. Bedau, Alastair Channon +4
Nature's spectacular inventiveness, reflected in the enormous diversity of form and function displayed by the biosphere, is a feature of life that distinguishes living most strongl…
Evolvability ES: Scalable and Direct Optimization of Evolvability
Alexander Gajewski, Jeff Clune, Kenneth O. Stanley +1
Designing evolutionary algorithms capable of uncovering highly evolvable representations is an open challenge; such evolvability is important because it accelerates evolution and e…
Deep Neuroevolution of Recurrent and Discrete World Models
Sebastian Risi, Kenneth O. Stanley
Neural architectures inspired by our own human cognitive system, such as the recently introduced world models, have been shown to outperform traditional deep reinforcement learning…
Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions
Rui Wang, Joel Lehman, Jeff Clune +1
While the history of machine learning so far largely encompasses a series of problems posed by researchers and algorithms that learn their solutions, an important question is wheth…
Go-Explore: a New Approach for Hard-Exploration Problems
Adrien Ecoffet, Joost Huizinga, Joel Lehman +2
A grand challenge in reinforcement learning is intelligent exploration, especially when rewards are sparse or deceptive. Two Atari games serve as benchmarks for such hard-explorati…