Search Results for author: Jeff Clune

Found 54 papers, 34 papers with code

Scaling Instructable Agents Across Many Simulated Worlds

no code implementations13 Mar 2024 SIMA Team, Maria Abi Raad, Arun Ahuja, Catarina Barros, Frederic Besse, Andrew Bolt, Adrian Bolton, Bethanie Brownfield, Gavin Buttimore, Max Cant, Sarah Chakera, Stephanie C. Y. Chan, Jeff Clune, Adrian Collister, Vikki Copeman, Alex Cullum, Ishita Dasgupta, Dario de Cesare, Julia Di Trapani, Yani Donchev, Emma Dunleavy, Martin Engelcke, Ryan Faulkner, Frankie Garcia, Charles Gbadamosi, Zhitao Gong, Lucy Gonzales, Kshitij Gupta, Karol Gregor, Arne Olav Hallingstad, Tim Harley, Sam Haves, Felix Hill, Ed Hirst, Drew A. Hudson, Jony Hudson, Steph Hughes-Fitt, Danilo J. Rezende, Mimi Jasarevic, Laura Kampis, Rosemary Ke, Thomas Keck, Junkyung Kim, Oscar Knagg, Kavya Kopparapu, Andrew Lampinen, Shane Legg, Alexander Lerchner, Marjorie Limont, YuLan Liu, Maria Loks-Thompson, Joseph Marino, Kathryn Martin Cussons, Loic Matthey, Siobhan Mcloughlin, Piermaria Mendolicchio, Hamza Merzic, Anna Mitenkova, Alexandre Moufarek, Valeria Oliveira, Yanko Oliveira, Hannah Openshaw, Renke Pan, Aneesh Pappu, Alex Platonov, Ollie Purkiss, David Reichert, John Reid, Pierre Harvey Richemond, Tyson Roberts, Giles Ruscoe, Jaume Sanchez Elias, Tasha Sandars, Daniel P. Sawyer, Tim Scholtes, Guy Simmons, Daniel Slater, Hubert Soyer, Heiko Strathmann, Peter Stys, Allison C. Tam, Denis Teplyashin, Tayfun Terzi, Davide Vercelli, Bojan Vujatovic, Marcus Wainwright, Jane X. Wang, Zhengdong Wang, Daan Wierstra, Duncan Williams, Nathaniel Wong, Sarah York, Nick Young

Building embodied AI systems that can follow arbitrary language instructions in any 3D environment is a key challenge for creating general AI.

Quality-Diversity through AI Feedback

no code implementations19 Oct 2023 Herbie Bradley, Andrew Dai, Hannah Teufel, Jenny Zhang, Koen Oostermeijer, Marco Bellagente, Jeff Clune, Kenneth Stanley, Grégory Schott, Joel Lehman

In many text-generation problems, users may prefer not only a single response, but a diverse range of high-quality outputs from which to choose.

Text Generation

Quality Diversity through Human Feedback

1 code implementation18 Oct 2023 Li Ding, Jenny Zhang, Jeff Clune, Lee Spector, Joel Lehman

Meanwhile, Quality Diversity (QD) algorithms excel at identifying diverse and high-quality solutions but often rely on manually crafted diversity metrics.

Image Generation reinforcement-learning +2

Reset It and Forget It: Relearning Last-Layer Weights Improves Continual and Transfer Learning

no code implementations12 Oct 2023 Lapo Frati, Neil Traft, Jeff Clune, Nick Cheney

We show that our zapping procedure results in improved transfer accuracy and/or more rapid adaptation in both standard fine-tuning and continual learning settings, while being simple to implement and computationally efficient.

Continual Learning Meta-Learning +1

First-Explore, then Exploit: Meta-Learning Intelligent Exploration

1 code implementation5 Jul 2023 Ben Norman, Jeff Clune

We argue a core barrier prohibiting many RL approaches from learning intelligent exploration is that the methods attempt to explore and exploit simultaneously, which harms both exploration and exploitation as the goals often conflict.

Meta-Learning Reinforcement Learning (RL)

OMNI: Open-endedness via Models of human Notions of Interestingness

1 code implementation2 Jun 2023 Jenny Zhang, Joel Lehman, Kenneth Stanley, Jeff Clune

An Achilles Heel of open-endedness research is the inability to quantify (and thus prioritize) tasks that are not just learnable, but also $\textit{interesting}$ (e. g., worthwhile and novel).

Thought Cloning: Learning to Think while Acting by Imitating Human Thinking

1 code implementation NeurIPS 2023 Shengran Hu, Jeff Clune

We hypothesize one reason for such cognitive deficiencies is that they lack the benefits of thinking in language and that we can improve AI agents by training them to think like humans do.

Imitation Learning Reinforcement Learning (RL)

Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos

2 code implementations23 Jun 2022 Bowen Baker, Ilge Akkaya, Peter Zhokhov, Joost Huizinga, Jie Tang, Adrien Ecoffet, Brandon Houghton, Raul Sampedro, Jeff Clune

Pretraining on noisy, internet-scale datasets has been heavily studied as a technique for training models with broad, general capabilities for text, images, and other modalities.

Imitation Learning reinforcement-learning +1

Continual learning under domain transfer with sparse synaptic bursting

no code implementations26 Aug 2021 Shawn L. Beaulieu, Jeff Clune, Nick Cheney

Past efforts to engineer such systems have sought to build or regulate artificial neural networks using disjoint sets of weights that are uniquely sensitive to specific tasks or inputs.

Continual Learning Meta-Learning

Open Questions in Creating Safe Open-ended AI: Tensions Between Control and Creativity

1 code implementation12 Jun 2020 Adrien Ecoffet, Jeff Clune, Joel Lehman

This paper proposes that open-ended evolution and artificial life have much to contribute towards the understanding of open-ended AI, focusing here in particular on the safety of open-ended search.

Artificial Life

Synthetic Petri Dish: A Novel Surrogate Model for Rapid Architecture Search

1 code implementation27 May 2020 Aditya Rawal, Joel Lehman, Felipe Petroski Such, Jeff Clune, Kenneth O. Stanley

Neural Architecture Search (NAS) explores a large space of architectural motifs -- a compute-intensive process that often involves ground-truth evaluation of each motif by instantiating it within a large network, and training and evaluating the network with thousands of domain-specific data samples.

Neural Architecture Search

First return, then explore

2 code implementations27 Apr 2020 Adrien Ecoffet, Joost Huizinga, Joel Lehman, Kenneth O. Stanley, Jeff Clune

The promise of reinforcement learning is to solve complex sequential decision problems autonomously by specifying a high-level reward function only.

Montezuma's Revenge reinforcement-learning +1

Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions

1 code implementation ICML 2020 Rui Wang, Joel Lehman, Aditya Rawal, Jiale Zhi, Yulun Li, Jeff Clune, Kenneth O. Stanley

Creating open-ended algorithms, which generate their own never-ending stream of novel and appropriately challenging learning opportunities, could help to automate and accelerate progress in machine learning.

Reinforcement Learning (RL)

Scaling MAP-Elites to Deep Neuroevolution

3 code implementations3 Mar 2020 Cédric Colas, Joost Huizinga, Vashisht Madhavan, Jeff Clune

Quality-Diversity (QD) algorithms, and MAP-Elites (ME) in particular, have proven very useful for a broad range of applications including enabling real robots to recover quickly from joint damage, solving strongly deceptive maze tasks or evolving robot morphologies to discover new gaits.

Efficient Exploration

Learning to Continually Learn

5 code implementations21 Feb 2020 Shawn Beaulieu, Lapo Frati, Thomas Miconi, Joel Lehman, Kenneth O. Stanley, Jeff Clune, Nick Cheney

Continual lifelong learning requires an agent or model to learn many sequentially ordered tasks, building on previous knowledge without catastrophically forgetting it.

Continual Learning Meta-Learning

A deep active learning system for species identification and counting in camera trap images

1 code implementation22 Oct 2019 Mohammad Sadegh Norouzzadeh, Dan Morris, Sara Beery, Neel Joshi, Nebojsa Jojic, Jeff Clune

However, the accuracy of results depends on the amount, quality, and diversity of the data available to train models, and the literature has focused on projects with millions of relevant, labeled training images.

Active Learning Decision Making +1

Evolvability ES: Scalable and Direct Optimization of Evolvability

1 code implementation13 Jul 2019 Alexander Gajewski, Jeff Clune, Kenneth O. Stanley, Joel Lehman

Designing evolutionary algorithms capable of uncovering highly evolvable representations is an open challenge; such evolvability is important because it accelerates evolution and enables fast adaptation to changing circumstances.

Evolutionary Algorithms Meta-Learning

AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence

no code implementations27 May 2019 Jeff Clune

Because it it may be the fastest path to general AI and because it is inherently scientifically interesting to understand the conditions in which a simple algorithm can produce general AI (as happened on Earth where Darwinian evolution produced human intelligence), I argue that the pursuit of AI-GAs should be considered a new grand challenge of computer science research.

Meta-Learning

Understanding Neural Networks via Feature Visualization: A survey

1 code implementation18 Apr 2019 Anh Nguyen, Jason Yosinski, Jeff Clune

A neuroscience method to understanding the brain is to find and study the preferred stimuli that highly activate an individual cell or groups of cells.

BIG-bench Machine Learning

Go-Explore: a New Approach for Hard-Exploration Problems

3 code implementations30 Jan 2019 Adrien Ecoffet, Joost Huizinga, Joel Lehman, Kenneth O. Stanley, Jeff Clune

Go-Explore can also harness human-provided domain knowledge and, when augmented with it, scores a mean of over 650k points on Montezuma's Revenge.

Imitation Learning Montezuma's Revenge

Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions

2 code implementations7 Jan 2019 Rui Wang, Joel Lehman, Jeff Clune, Kenneth O. Stanley

Our results show that POET produces a diverse range of sophisticated behaviors that solve a wide range of environmental challenges, many of which cannot be solved by direct optimization alone, or even through a direct-path curriculum-building control algorithm introduced to highlight the critical role of open-endedness in solving ambitious challenges.

Robustness to Out-of-Distribution Inputs via Task-Aware Generative Uncertainty

no code implementations27 Dec 2018 Rowan McAllister, Gregory Kahn, Jeff Clune, Sergey Levine

Our method estimates an uncertainty measure about the model's prediction, taking into account an explicit (generative) model of the observation distribution to handle out-of-distribution inputs.

An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents

1 code implementation17 Dec 2018 Felipe Petroski Such, Vashisht Madhavan, Rosanne Liu, Rui Wang, Pablo Samuel Castro, Yulun Li, Jiale Zhi, Ludwig Schubert, Marc G. Bellemare, Jeff Clune, Joel Lehman

We lessen this friction, by (1) training several algorithms at scale and releasing trained models, (2) integrating with a previous Deep RL model release, and (3) releasing code that makes it easy for anyone to load, visualize, and analyze such models.

Atari Games Friction +2

Evolving Multimodal Robot Behavior via Many Stepping Stones with the Combinatorial Multi-Objective Evolutionary Algorithm

3 code implementations9 Jul 2018 Joost Huizinga, Jeff Clune

Lastly, we show that, in contrast to NSGA-II and Lexicase Selection, CMOEA can effectively leverage secondary objectives to achieve state-of-the-art results on the robotics task.

Deep Curiosity Search: Intra-Life Exploration Can Improve Performance on Challenging Deep Reinforcement Learning Problems

no code implementations1 Jun 2018 Christopher Stanton, Jeff Clune

The strong performance of DeepCS on these sparse- and dense-reward tasks suggests that encouraging intra-life novelty is an interesting, new approach for improving performance in Deep RL and motivates further research into hybridizing across-training and intra-life exploration methods.

Montezuma's Revenge

VINE: An Open Source Interactive Data Visualization Tool for Neuroevolution

1 code implementation3 May 2018 Rui Wang, Jeff Clune, Kenneth O. Stanley

Recent advances in deep neuroevolution have demonstrated that evolutionary algorithms, such as evolution strategies (ES) and genetic algorithms (GA), can scale to train deep neural networks to solve difficult reinforcement learning (RL) problems.

Data Visualization Evolutionary Algorithms +1

Differentiable plasticity: training plastic neural networks with backpropagation

5 code implementations ICML 2018 Thomas Miconi, Jeff Clune, Kenneth O. Stanley

How can we build agents that keep learning from experience, quickly and efficiently, after their initial training?

Meta-Learning

On the Relationship Between the OpenAI Evolution Strategy and Stochastic Gradient Descent

no code implementations18 Dec 2017 Xingwen Zhang, Jeff Clune, Kenneth O. Stanley

Because stochastic gradient descent (SGD) has shown promise optimizing neural networks with millions of parameters and few if any alternatives are known to exist, it has moved to the heart of leading approaches to reinforcement learning (RL).

Reinforcement Learning (RL)

Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents

2 code implementations NeurIPS 2018 Edoardo Conti, Vashisht Madhavan, Felipe Petroski Such, Joel Lehman, Kenneth O. Stanley, Jeff Clune

Evolution strategies (ES) are a family of black-box optimization algorithms able to train deep neural networks roughly as well as Q-learning and policy gradient methods on challenging deep reinforcement learning (RL) problems, but are much faster (e. g. hours vs. days) because they parallelize better.

Policy Gradient Methods Q-Learning +2

Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning

14 code implementations18 Dec 2017 Felipe Petroski Such, Vashisht Madhavan, Edoardo Conti, Joel Lehman, Kenneth O. Stanley, Jeff Clune

Here we demonstrate they can: we evolve the weights of a DNN with a simple, gradient-free, population-based genetic algorithm (GA) and it performs well on hard deep RL problems, including Atari and humanoid locomotion.

Evolutionary Algorithms Q-Learning +1

Safe Mutations for Deep and Recurrent Neural Networks through Output Gradients

1 code implementation18 Dec 2017 Joel Lehman, Jay Chen, Jeff Clune, Kenneth O. Stanley

While neuroevolution (evolving neural networks) has a successful track record across a variety of domains from reinforcement learning to artificial life, it is rarely applied to large, deep neural networks.

Artificial Life

ES Is More Than Just a Traditional Finite-Difference Approximator

no code implementations18 Dec 2017 Joel Lehman, Jay Chen, Jeff Clune, Kenneth O. Stanley

However, this ES optimizes for a different gradient than just reward: It optimizes for the average reward of the entire population, thereby seeking parameters that are robust to perturbation.

reinforcement-learning Reinforcement Learning (RL)

Diffusion-based neuromodulation can eliminate catastrophic forgetting in simple neural networks

no code implementations20 May 2017 Roby Velez, Jeff Clune

On the simple diagnostic problem from the prior work, diffusion-based neuromodulation 1) induces task-specific learning in groups of nodes and connections (task-specific localized learning), which 2) produces functional modules for each subtask, and 3) yields higher performance by eliminating catastrophic forgetting.

The Emergence of Canalization and Evolvability in an Open-Ended, Interactive Evolutionary System

1 code implementation17 Apr 2017 Joost Huizinga, Kenneth O. Stanley, Jeff Clune

In this paper we reveal a unique system in which canalization did emerge in computational evolution.

Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning

no code implementations16 Mar 2017 Mohammed Sadegh Norouzzadeh, Anh Nguyen, Margaret Kosmala, Ali Swanson, Meredith Palmer, Craig Packer, Jeff Clune

Having accurate, detailed, and up-to-date information about the location and behavior of animals in the wild would revolutionize our ability to study and conserve ecosystems.

Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space

1 code implementation CVPR 2017 Anh Nguyen, Jeff Clune, Yoshua Bengio, Alexey Dosovitskiy, Jason Yosinski

PPGNs are composed of 1) a generator network G that is capable of drawing a wide range of image types and 2) a replaceable "condition" network C that tells the generator what to draw.

Image Captioning Image Inpainting

Synthesizing the preferred inputs for neurons in neural networks via deep generator networks

5 code implementations NeurIPS 2016 Anh Nguyen, Alexey Dosovitskiy, Jason Yosinski, Thomas Brox, Jeff Clune

Understanding the inner workings of such computational brains is both fascinating basic science that is interesting in its own right - similar to why we study the human brain - and will enable researchers to further improve DNNs.

Multifaceted Feature Visualization: Uncovering the Different Types of Features Learned By Each Neuron in Deep Neural Networks

no code implementations11 Feb 2016 Anh Nguyen, Jason Yosinski, Jeff Clune

Here, we introduce an algorithm that explicitly uncovers the multiple facets of each neuron by producing a synthetic visualization of each of the types of images that activate a neuron.

Convergent Learning: Do different neural networks learn the same representations?

1 code implementation24 Nov 2015 Yixuan Li, Jason Yosinski, Jeff Clune, Hod Lipson, John Hopcroft

Recent success in training deep neural networks have prompted active investigation into the features learned on their intermediate layers.

Clustering

Understanding Neural Networks Through Deep Visualization

7 code implementations22 Jun 2015 Jason Yosinski, Jeff Clune, Anh Nguyen, Thomas Fuchs, Hod Lipson

The first is a tool that visualizes the activations produced on each layer of a trained convnet as it processes an image or video (e. g. a live webcam stream).

Interpretable Machine Learning

The evolutionary origins of hierarchy

no code implementations23 May 2015 Henok Mengistu, Joost Huizinga, Jean-Baptiste Mouret, Jeff Clune

Hierarchical organization -- the recursive composition of sub-modules -- is ubiquitous in biological networks, including neural, metabolic, ecological, and genetic regulatory networks, and in human-made systems, such as large organizations and the Internet.

Illuminating search spaces by mapping elites

6 code implementations20 Apr 2015 Jean-Baptiste Mouret, Jeff Clune

Interestingly, because MAP-Elites explores more of the search space, it also tends to find a better overall solution than state-of-the-art search algorithms.

Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images

2 code implementations CVPR 2015 Anh Nguyen, Jason Yosinski, Jeff Clune

Here we show a related result: it is easy to produce images that are completely unrecognizable to humans, but that state-of-the-art DNNs believe to be recognizable objects with 99. 99% confidence (e. g. labeling with certainty that white noise static is a lion).

Evolutionary Algorithms

How transferable are features in deep neural networks?

3 code implementations NeurIPS 2014 Jason Yosinski, Jeff Clune, Yoshua Bengio, Hod Lipson

Such first-layer features appear not to be specific to a particular dataset or task, but general in that they are applicable to many datasets and tasks.

Specificity

Robots that can adapt like animals

2 code implementations13 Jul 2014 Antoine Cully, Jeff Clune, Danesh Tarapore, Jean-Baptiste Mouret

As robots leave the controlled environments of factories to autonomously function in more complex, natural environments, they will have to respond to the inevitable fact that they will become damaged.

Hands-free Evolution of 3D-printable Objects via Eye Tracking

no code implementations17 Apr 2013 Nick Cheney, Jeff Clune, Jason Yosinski, Hod Lipson

Interactive evolution has shown the potential to create amazing and complex forms in both 2-D and 3-D settings.

The evolutionary origins of modularity

no code implementations11 Jul 2012 Jeff Clune, Jean-Baptiste Mouret, Hod Lipson

A central biological question is how natural organisms are so evolvable (capable of quickly adapting to new environments).

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