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06/02/2023
OMNI: Open-endedness via Models of human Notions of Interestingness
Open-ended algorithms aim to learn new, interesting behaviors forever. T...
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02/23/2023
Language Model Crossover: Variation through Few-Shot Prompting
This paper pursues the insight that language models naturally enable an ...
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02/18/2023
Machine Love
While ML generates much economic value, many of us have problematic rela...
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06/17/2022
Evolution through Large Models
This paper pursues the insight that large language models (LLMs) trained...
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07/21/2020
Ideas for Improving the Field of Machine Learning: Summarizing Discussion from the NeurIPS 2019 Retrospectives Workshop
This report documents ideas for improving the field of machine learning,...
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06/12/2020
Open Questions in Creating Safe Open-ended AI: Tensions Between Control and Creativity
Artificial life originated and has long studied the topic of open-ended ...
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06/08/2020
Reinforcement Learning Under Moral Uncertainty
An ambitious goal for artificial intelligence is to create agents that b...
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05/27/2020
Synthetic Petri Dish: A Novel Surrogate Model for Rapid Architecture Search
Neural Architecture Search (NAS) explores a large space of architectural...
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04/27/2020
First return then explore
The promise of reinforcement learning is to solve complex sequential dec...
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03/19/2020
Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions
Creating open-ended algorithms, which generate their own never-ending st...
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02/21/2020
Learning to Continually Learn
Continual lifelong learning requires an agent or model to learn many seq...
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12/17/2019
Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data
This paper investigates the intriguing question of whether we can create...
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07/13/2019
Evolvability ES: Scalable and Direct Optimization of Evolvability
Designing evolutionary algorithms capable of uncovering highly evolvable...
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06/26/2019
Towards Empathic Deep Q-Learning
As reinforcement learning (RL) scales to solve increasingly complex task...
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06/24/2019
Evolutionary Computation and AI Safety: Research Problems Impeding Routine and Safe Real-world Application of Evolution
Recent developments in artificial intelligence and machine learning have...
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06/22/2019
Learning Belief Representations for Imitation Learning in POMDPs
We consider the problem of imitation learning from expert demonstrations...
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01/30/2019
Go-Explore: a New Approach for Hard-Exploration Problems
A grand challenge in reinforcement learning is intelligent exploration, ...
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01/07/2019
Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions
While the history of machine learning so far encompasses a series of pro...
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12/17/2018
An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents
Much human and computational effort has aimed to improve how deep reinfo...
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07/09/2018
An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution
Few ideas have enjoyed as large an impact on deep learning as convolutio...
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03/09/2018
The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities
Biological evolution provides a creative fount of complex and subtle ada...
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12/18/2017
ES Is More Than Just a Traditional Finite-Difference Approximator
An evolution strategy (ES) variant recently attracted significant attent...
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12/18/2017
Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning
Deep artificial neural networks (DNNs) are typically trained via gradien...
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12/18/2017
Safe Mutations for Deep and Recurrent Neural Networks through Output Gradients
While neuroevolution (evolving neural networks) has a successful track r...
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12/18/2017
Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents
Evolution strategies (ES) are a family of black-box optimization algorit...
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04/26/2016
Using Indirect Encoding of Multiple Brains to Produce Multimodal Behavior
An important challenge in neuroevolution is to evolve complex neural net...
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02/05/2013