Ha, David

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.

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.

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.

Learning Latent Dynamics for Planning from Pixels

February 15, 2019

PlaNet learns a world model from image inputs only and successfully leverages it for planning in latent space.

Reinforcement Learning for Improving Agent Design

October 10, 2018

Little dude rewarded for having little legs.