Reinforcement learning (RL), particularly its combination with deep neural networks,
referred to as deep RL (DRL), has shown tremendous promise across a wide range of …

Recent improvements in conditional generative modeling have made it possible to generate
high-quality images from language descriptions alone. We investigate whether these …

While deep reinforcement learning (RL) has fueled multiple high-profile successes in
machine learning, it is held back from more widespread adoption by its often poor data …

The pre-train and fine-tune paradigm in machine learning has had dramatic success in a
wide range of domains because the use of existing data or pre-trained models on the …

We propose VRL3, a powerful data-driven framework with a simple design for solving
challenging visual deep reinforcement learning (DRL) tasks. We analyze a number of major …

We present a system that enables an autonomous small-scale RC car to drive aggressively
from visual observations using reinforcement learning (RL). Our system, FastRLAP, trains …

Despite the promising performance of deep reinforcement learning (DRL) agents in many
challenging scenarios, the black-box nature of these agents greatly limits their applications …

Intelligent instruction-following robots capable of improving from autonomously collected
experience have the potential to transform robot learning: instead of collecting costly …

Reinforcement Learning (RL) algorithms can solve challenging control problems directly
from image observations, but they often require millions of environment interactions to do so …

Recent works have shown the potential of diffusion models in computer vision and natural
language processing. Apart from the classical supervised learning fields, diffusion models …