Search Results for author:
Found 8 papers, 4 papers with code
Unsupervised Zero-Shot Reinforcement Learning via Functional Reward Encodings
Can we pre-train a generalist agent from a large amount of unlabeled offline trajectories such that it can be immediately adapted to any new downstream tasks in a zero-shot manner?
Powderworld: A Platform for Understanding Generalization via Rich Task Distributions
Within Powderworld, two motivating challenges distributions are presented, one for world-modelling and one for reinforcement learning.
CLIPDraw: Exploring Text-to-Drawing Synthesis through Language-Image Encoders
This work presents CLIPDraw, an algorithm that synthesizes novel drawings based on natural language input.
Selecting for Selection: Learning To Balance Adaptive and Diversifying Pressures in Evolutionary Search
Inspired by natural evolution, evolutionary search algorithms have proven remarkably capable due to their dual abilities to radiantly explore through diverse populations and to converge to adaptive pressures.
Population-Based Evolution Optimizes a Meta-Learning Objective
Meta-learning models, or models that learn to learn, have been a long-desired target for their ability to quickly solve new tasks.
Unsupervised Image to Sequence Translation with Canvas-Drawer Networks
Encoding images as a series of high-level constructs, such as brush strokes or discrete shapes, can often be key to both human and machine understanding.
Meta Learning Shared Hierarchies
We develop a metalearning approach for learning hierarchically structured policies, improving sample efficiency on unseen tasks through the use of shared primitives---policies that are executed for large numbers of timesteps.
Outline Colorization through Tandem Adversarial Networks
When creating digital art, coloring and shading are often time consuming tasks that follow the same general patterns.
