Explainable reinforcement learning (XRL) is an emerging subfield of explainable machine
learning that has attracted considerable attention in recent years. The goal of XRL is to …

Transformers have emerged as the architecture of choice for many state-of-the-art AI
models, showcasing exceptional performance across a wide range of AI applications …

Visual reinforcement learning (RL), which makes decisions directly from high-dimensional
visual inputs, has demonstrated significant potential in various domains. However …

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 …

Abstract We introduce Masked Trajectory Models (MTM) as a generic abstraction for
sequential decision making. MTM takes a trajectory, such as a state-action sequence, and …

Reinforcement learning (RL) requires access to a reward function that incentivizes the right
behavior, but these are notoriously hard to specify for complex tasks. Preference-based RL …

Transformers have emerged as the cornerstone of state-of-the-art natural language
processing models, showcasing exceptional performance across a wide range of AI …

Unsupervised pre-training methods utilizing large and diverse datasets have achieved
tremendous success across a range of domains. Recent work has investigated such …

Large transformer models powered by diverse data and model scale have dominated
natural language modeling and computer vision and pushed the frontier of multiple AI areas …

One of the key capabilities of intelligent agents is the ability to discover useful skills without
external supervision. However, the current unsupervised skill discovery methods are often …