544 citations · 1.7k across the 27 of their papers we have counts for
7 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…
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…
Towards Empathic Deep Q-Learning
Bart Bussmann, Jacqueline Heinerman, Joel Lehman
As reinforcement learning (RL) scales to solve increasingly complex tasks, interest continues to grow in the fields of AI safety and machine ethics. As a contribution to these fiel…
Learning Belief Representations for Imitation Learning in POMDPs
Tanmay Gangwani, Joel Lehman, Qiang Liu +1
We consider the problem of imitation learning from expert demonstrations in partially observable Markov decision processes (POMDPs). Belief representations, which characterize the…
Evolutionary Computation and AI Safety: Research Problems Impeding Routine and Safe Real-world Application of Evolution
Joel Lehman
Recent developments in artificial intelligence and machine learning have spurred interest in the growing field of AI safety, which studies how to prevent human-harming accidents wh…
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…