Unsupervised pre-training has recently become the bedrock for computer vision and natural
language processing. In reinforcement learning (RL), goal-conditioned RL can potentially …

Goal-conditioned reinforcement learning (GCRL), related to a set of complex RL problems,
trains an agent to achieve different goals under particular scenarios. Compared to the …

In goal-reaching reinforcement learning (RL), the optimal value function has a particular
geometry, called quasimetrics structure. This paper introduces Quasimetric Reinforcement …

Unsupervised pre-training strategies have proven to be highly effective in natural language
processing and computer vision. Likewise, unsupervised reinforcement learning (RL) holds …

Temporal distances lie at the heart of many algorithms for planning, control, and
reinforcement learning that involve reaching goals, allowing one to estimate the transit time …

Aligning language models (LMs) with preferences is an important problem in natural
language generation. A key challenge is that preferences are typically provided at the …

Transferring human motion skills to humanoid robots remains a significant challenge. In this
study, we introduce a Wasserstein adversarial imitation learning system, allowing humanoid …

Offline goal-conditioned reinforcement learning (GCRL) promises general-purpose skill
learning in the form of reaching diverse goals from purely offline datasets. We propose …

Predicting and reasoning about the future lie at the heart of many time-series questions. For
example, goal-conditioned reinforcement learning can be viewed as learning …

When learning task-oriented dialogue (ToD) agents, reinforcement learning (RL) techniques
can naturally be utilized to train dialogue strategies to achieve user-specific goals. Prior …