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Found 8 papers, 2 papers with code
When Does Neuroevolution Outcompete Reinforcement Learning in Transfer Learning Tasks?
The ability to continuously and efficiently transfer skills across tasks is a hallmark of biological intelligence and a long-standing goal in artificial systems.
Bio-Inspired Plastic Neural Networks for Zero-Shot Out-of-Distribution Generalization in Complex Animal-Inspired Robots
In this work, we improve the Hebbian network with a weight normalization mechanism for preventing weight divergence, analyze the principal components of the Hebbian's weights, and perform a thorough evaluation of network performance in locomotion control for real 18-DOF dung beetle-like and 16-DOF gecko-like robots.
From Text to Life: On the Reciprocal Relationship between Artificial Life and Large Language Models
Large Language Models (LLMs) have taken the field of AI by storm, but their adoption in the field of Artificial Life (ALife) has been, so far, relatively reserved.
Growing Artificial Neural Networks for Control: the Role of Neuronal Diversity
Artificial neural networks (ANNs), on the other hand, are traditionally optimized in the space of weights.
Structurally Flexible Neural Networks: Evolving the Building Blocks for General Agents
Artificial neural networks used for reinforcement learning are structurally rigid, meaning that each optimized parameter of the network is tied to its specific placement in the network structure.
Learning to Act through Evolution of Neural Diversity in Random Neural Networks
Biological nervous systems consist of networks of diverse, sophisticated information processors in the form of neurons of different classes.
Minimal Neural Network Models for Permutation Invariant Agents
Organisms in nature have evolved to exhibit flexibility in face of changes to the environment and/or to themselves.
Evolving and Merging Hebbian Learning Rules: Increasing Generalization by Decreasing the Number of Rules
Inspired by the biological phenomenon of the genomic bottleneck, we show that by allowing multiple connections in the network to share the same local learning rule, it is possible to drastically reduce the number of trainable parameters, while obtaining a more robust agent.
