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 …

Learning good feature representations is important for deep reinforcement learning (RL).
However, with limited experience, RL often suffers from data inefficiency for training. For un …

Decision Transformer (DT) is an innovative algorithm leveraging recent advances of the
transformer architecture in reinforcement learning (RL). However, a notable limitation of DT …

Recent advancements in offline reinforcement learning (RL) have underscored the
capabilities of Return-Conditioned Supervised Learning (RCSL), a paradigm that learns the …

Abstract Model-based reinforcement learning (RL) has shown great potential in various
control tasks in terms of both sample-efficiency and final performance. However, learning a …

Recent advancements in offline reinforcement learning (RL) have underscored the
capabilities of Conditional Sequence Modeling (CSM), a paradigm that learns the action …

In reinforcement learning (RL), representation learning is a proven tool for complex image-
based tasks, but is often overlooked for environments with low-level states, such as physical …

Transformers hold tremendous promise in solving offline reinforcement learning (RL) by
formulating it as a sequence modeling problem inspired by language modeling (LM). Prior …

Reinforcement learning (RL) is a branch of machine learning which is employed to solve
various sequential decision making problems without proper supervision. Due to the recent …

Deep reinforcement learning algorithms that learn policies by trial-and-error must learn from
limited amounts of data collected by actively interacting with the environment. While many …