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Permutation-Invariant Neural Networks for Reinforcement Learning

November 18, 2021


Reinforcement learning agents typically perform poorly if provided with inputs that were not clearly defined in training. A new approach enables RL agents to perform well, even when subject to corrupt, incomplete, or shuffled inputs.


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Modern Evolution Strategies for Creativity:
Fitting Concrete Images and Abstract Concepts

September 21, 2021


“A drawing of a cat”

CLIP + ES + Triangles


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Neuroevolution of Self-Interpretable Agents

March 18, 2020


Agents with a self-attention “bottleneck” not only can solve these tasks from pixel inputs with only 4000 parameters, but they are also better at generalization.


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Learning to Predict Without Looking Ahead

October 29, 2019


Rather than hardcoding forward prediction, we try to get agents to learn that they need to predict the future.


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Weight Agnostic Neural Networks

June 12, 2019


We search for neural network architectures that can already perform various tasks even when they use random weight values.


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