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In contrast to deep reinforcement learning agents, biological neural networks are grown through a self-organized developmental process.
Theory of self-reproducing automata , volume 1102024
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Learning cellular automaton dynamics with neural networks
N Wulff and J A Hertz · 1992
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Adapting arbitrary normal mutation distributions in evolution strategies: the covariance matrix adaptation
N. Hansen and A. Ostermeier · 1996
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A taxonomy for artificial embryogeny
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Neural Cellular Automata Manifold
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Neuro-cellular automata: Connecting cellular automata, neural networks and evolution
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Evolving coordinated quadruped gaits with the hyperneat generative encoding
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A hypercube-based encoding for evolving large-scale neural networks
Kenneth O Stanley, David B D’Ambrosio, and Jason Gauci · 2009
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Morphological change in machines accelerates the evolution of robust behavior
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Active learning of inverse models with intrinsically motivated goal exploration in robots
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Evolving large-scale neural networks for vision-based reinforcement learning
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Confronting the challenge of learning a flexible neural controller for a diversity of morphologies
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Deep neuroevolution of recurrent and discrete world models
Sebastian Risi and Kenneth O Stanley · 2019
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A critique of pure learning and what artificial neural networks can learn from animal brains
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Christian Carvelli, Djordje Grbic, and Sebastian Risi · 2020
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Yan Duan, Xi Chen, Rein Houthooft, John Schulman, and Pieter Abbeel · 2016
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David Ha, Andrew Dai, and Quoc V. Le · 2016
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Quality diversity: A new frontier for evolutionary computation
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The future of artificial intelligence is self-organizing and self-assembling
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Growing 3D Artefacts and Functional Machines with Neural Cellular Automata
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