Search Results for author: Elias Najarro

Found 7 papers, 7 papers with code

Goal-Guided Neural Cellular Automata: Learning to Control Self-Organising Systems

1 code implementation25 Apr 2022 Shyam Sudhakaran, Elias Najarro, Sebastian Risi

Inspired by cellular growth and self-organization, Neural Cellular Automata (NCAs) have been capable of "growing" artificial cells into images, 3D structures, and even functional machines.

HyperNCA: Growing Developmental Networks with Neural Cellular Automata

1 code implementation25 Apr 2022 Elias Najarro, Shyam Sudhakaran, Claire Glanois, Sebastian Risi

In contrast to deep reinforcement learning agents, biological neural networks are grown through a self-organized developmental process.

reinforcement-learning

A Unified Substrate for Body-Brain Co-evolution

1 code implementation22 Mar 2022 Sidney Pontes-Filho, Kathryn Walker, Elias Najarro, Stefano Nichele, Sebastian Risi

The genome of such a multicellular organism guides the development of its body from a single cell, including its control system.

Growing 3D Artefacts and Functional Machines with Neural Cellular Automata

1 code implementation15 Mar 2021 Shyam Sudhakaran, Djordje Grbic, Siyan Li, Adam Katona, Elias Najarro, Claire Glanois, Sebastian Risi

Neural Cellular Automata (NCAs) have been proven effective in simulating morphogenetic processes, the continuous construction of complex structures from very few starting cells.

EvoCraft: A New Challenge for Open-Endedness

1 code implementation8 Dec 2020 Djordje Grbic, Rasmus Berg Palm, Elias Najarro, Claire Glanois, Sebastian Risi

In contrast to previous work in Minecraft that focused on learning to play the game, the grand challenge we pose here is to automatically search for increasingly complex artifacts in an open-ended fashion.

Testing the Genomic Bottleneck Hypothesis in Hebbian Meta-Learning

1 code implementation13 Nov 2020 Rasmus Berg Palm, Elias Najarro, Sebastian Risi

We test this hypothesis by decoupling the number of Hebbian learning rules from the number of synapses and systematically varying the number of Hebbian learning rules.

Meta-Learning

Meta-Learning through Hebbian Plasticity in Random Networks

4 code implementations NeurIPS 2020 Elias Najarro, Sebastian Risi

We find that starting from completely random weights, the discovered Hebbian rules enable an agent to navigate a dynamical 2D-pixel environment; likewise they allow a simulated 3D quadrupedal robot to learn how to walk while adapting to morphological damage not seen during training and in the absence of any explicit reward or error signal in less than 100 timesteps.

reinforcement-learning

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