Recent work has shown that supervised learning alone, without temporal difference (TD)
learning, can be remarkably effective for offline RL. When does this hold true, and which …

Despite the recent advancements in offline reinforcement learning via supervised learning
(RvS) and the success of the decision transformer (DT) architecture in various domains, DTs …

Abstract Model-based offline reinforcement learning (RL), which builds a supervised
transition model with logging dataset to avoid costly interactions with the online …

Adaptive interfaces can help users perform sequential decision-making tasks like robotic
teleoperation given noisy, high-dimensional command signals (eg, from a brain-computer …

Pessimism is of great importance in offline reinforcement learning (RL). One broad category
of offline RL algorithms fulfills pessimism by explicit or implicit behavior regularization …