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Found 31 papers, 20 papers with code
Automating the Search for Artificial Life with Foundation Models
With the recent Nobel Prize awarded for radical advances in protein discovery, foundation models (FMs) for exploring large combinatorial spaces promise to revolutionize many scientific fields.
Evolution through Large Models
This paper pursues the insight that large language models (LLMs) trained to generate code can vastly improve the effectiveness of mutation operators applied to programs in genetic programming (GP).
Towards Consistent Predictive Confidence through Fitted Ensembles
Furthermore, we present a new strong baseline for more consistent predictive confidence in deep models, called fitted ensembles, where overconfident predictions are rectified by transformed versions of the original classification task.
Synthetic Petri Dish: A Novel Surrogate Model for Rapid Architecture Search
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.
First return, then explore
The promise of reinforcement learning is to solve complex sequential decision problems autonomously by specifying a high-level reward function only.
Ranked #1 on
Atari Games
on Atari 2600 Montezuma's Revenge
Fiber: A Platform for Efficient Development and Distributed Training for Reinforcement Learning and Population-Based Methods
Recent advances in machine learning are consistently enabled by increasing amounts of computation.
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 stream of novel and appropriately challenging learning opportunities, could help to automate and accelerate progress in machine learning.
Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity
We show that neuromodulated plasticity improves the performance of neural networks on both reinforcement learning and supervised learning tasks.
Learning to Continually Learn
Continual lifelong learning requires an agent or model to learn many sequentially ordered tasks, building on previous knowledge without catastrophically forgetting it.
Deep Innovation Protection: Confronting the Credit Assignment Problem in Training Heterogeneous Neural Architectures
Deep reinforcement learning approaches have shown impressive results in a variety of different domains, however, more complex heterogeneous architectures such as world models require the different neural components to be trained separately instead of end-to-end.
Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data
This paper introduces GTNs, discusses their potential, and showcases that they can substantially accelerate learning.
Deep Innovation Protection
Evolutionary-based optimization approaches have recently shown promising results in domains such as Atari and robot locomotion but less so in solving 3D tasks directly from pixels.
An Overview of Open-Ended Evolution: Editorial Introduction to the Open-Ended Evolution II Special Issue
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 strongly from nonliving.
Evolvability ES: Scalable and Direct Optimization of Evolvability
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.
Deep Neuroevolution of Recurrent and Discrete World Models
Instead of the relatively simple architectures employed in most RL experiments, world models rely on multiple different neural components that are responsible for visual information processing, memory, and decision-making.
Go-Explore: a New Approach for Hard-Exploration Problems
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.
Ranked #1 on
Atari Games
on Atari 2600 Pitfall!
Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions
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.
VINE: An Open Source Interactive Data Visualization Tool for Neuroevolution
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.
Differentiable plasticity: training plastic neural networks with backpropagation
How can we build agents that keep learning from experience, quickly and efficiently, after their initial training?
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 adaptations, often surprising the scientists who discover them.
Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning
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.
On the Relationship Between the OpenAI Evolution Strategy and Stochastic Gradient Descent
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).
Safe Mutations for Deep and Recurrent Neural Networks through Output Gradients
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.
ES Is More Than Just a Traditional Finite-Difference Approximator
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.
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 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.
The Emergence of Canalization and Evolvability in an Open-Ended, Interactive Evolutionary System
In this paper we reveal a unique system in which canalization did emerge in computational evolution.
Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks
Inspired by such intricate natural phenomena, Evolved Plastic Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed plastic neural networks with a large variety of dynamics, architectures, and plasticity rules: these artificial systems are composed of inputs, outputs, and plastic components that change in response to experiences in an environment.
Fitted Learning: Models with Awareness of their Limits
Though deep learning has pushed the boundaries of classification forward, in recent years hints of the limits of standard classification have begun to emerge.
A Proposed Infrastructure for Adding Online Interaction to Any Evolutionary Domain
To address the difficulty of creating online collaborative evolutionary systems, this paper presents a new prototype library called Worldwide Infrastructure for Neuroevolution (WIN) and its accompanying site WIN Online (http://winark. org/).
Unsupervised Feature Learning through Divergent Discriminative Feature Accumulation
Unlike unsupervised approaches such as autoencoders that learn to reconstruct their inputs, this paper introduces an alternative approach to unsupervised feature learning called divergent discriminative feature accumulation (DDFA) that instead continually accumulates features that make novel discriminations among the training set.
Evolving Neural Networks through Augmenting Topologies
An important question in neuroevolution is how to gain an advantage from evolving neural network topologies along with weights.
