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Found 6 papers, 2 papers with code
Is Self-Supervised Learning More Robust Than Supervised Learning?
Our insight has implications in improving the downstream robustness of supervised learning.
Shuffle Augmentation of Features from Unlabeled Data for Unsupervised Domain Adaptation
Unsupervised Domain Adaptation (UDA), a branch of transfer learning where labels for target samples are unavailable, has been widely researched and developed in recent years with the help of adversarially trained models.
Why Does Hierarchy (Sometimes) Work So Well in Reinforcement Learning?
Hierarchical reinforcement learning has demonstrated significant success at solving difficult reinforcement learning (RL) tasks.
Modular Architecture for StarCraft II with Deep Reinforcement Learning
We present a novel modular architecture for StarCraft II AI.
Reinforcement Learning with Deep Energy-Based Policies
We propose a method for learning expressive energy-based policies for continuous states and actions, which has been feasible only in tabular domains before.
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning
In this work, we describe a surprising finding: a simple generalization of the classic count-based approach can reach near state-of-the-art performance on various high-dimensional and/or continuous deep RL benchmarks.
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