Contrastive Learning has recently received interest due to its success in self-supervised
representation learning in the computer vision domain. However, the origins of Contrastive …

Energy systems undergo major transitions to facilitate the large-scale penetration of
renewable energy technologies and improve efficiencies, leading to the integration of many …

Developing a general algorithm that learns to solve tasks across a wide range of
applications has been a fundamental challenge in artificial intelligence. Although current …

Abstract We present CURL: Contrastive Unsupervised Representations for Reinforcement
Learning. CURL extracts high-level features from raw pixels using contrastive learning and …

In reinforcement learning (RL), it is easier to solve a task if given a good representation.
While deep RL should automatically acquire such good representations, prior work often …

Visual model-based reinforcement learning (RL) has the potential to enable sample-efficient
robot learning from visual observations. Yet the current approaches typically train a single …

Recent unsupervised pre-training methods have shown to be effective on language and
vision domains by learning useful representations for multiple downstream tasks. In this …

While deep reinforcement learning excels at solving tasks where large amounts of data can
be collected through virtually unlimited interaction with the environment, learning from …

Sequential decision making, commonly formalized as Markov Decision Process (MDP)
optimization, is an important challenge in artificial intelligence. Two key approaches to this …

We introduce a new unsupervised pre-training method for reinforcement learning called
APT, which stands for Active Pre-Training. APT learns behaviors and representations by …