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 …

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 …

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 …

In recent decades, wildfires have caused tremendous property losses, fatalities, and
extensive damage to forest ecosystems. Inspired by the abundance of publicly available …

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 …

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

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

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 …

Discovering achievements with a hierarchical structure in procedurally generated
environments presents a significant challenge. This requires an agent to possess a broad …

The difficulty of appropriately assigning credit is particularly heightened in cooperative
MARL with sparse reward, due to the concurrent time and structural scales involved …