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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.
