The reinforcement learning (RL) research area is very active, with an important number of
new contributions, especially considering the emergent field of deep RL (DRL). However, a …

Specifying reward signals that allow agents to learn complex behaviors is a long-standing
challenge in reinforcement learning. A promising approach is to extract preferences for …

Despite recent progress in reinforcement learning (RL) from raw pixel data, sample
inefficiency continues to present a substantial obstacle. Prior works have attempted to …

We consider a hybrid reinforcement learning setting (Hybrid RL), in which an agent has
access to an offline dataset and the ability to collect experience via real-world online …

Given time series data, how can we answer questions like what will happen in the
future?''and how did we get here?''These sorts of probabilistic inference questions are …

Self-supervised learning, dubbed the dark matter of intelligence, is a promising path to
advance machine learning. Yet, much like cooking, training SSL methods is a delicate art …

We study the learning dynamics of self-predictive learning for reinforcement learning, a
family of algorithms that learn representations by minimizing the prediction error of their own …

Annotation tools are an essential component in the creation of datasets for machine learning
purposes. Annotation tools have evolved greatly since the turn of the century, and now …

Representations are at the core of all deep reinforcement learning (RL) methods for both
Markov decision processes (MDPs) and partially observable Markov decision processes …

For deep reinforcement learning (RL) from pixels, learning effective state representations is
crucial for achieving high performance. However, in practice, limited experience and high …