Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of
Generative Artificial Intelligence (GAI), demonstrating their versatility and efficacy across a …

Diffusion models have demonstrated highly-expressive generative capabilities in vision and
NLP. Recent studies in reinforcement learning (RL) have shown that diffusion models are …

Diffusion models are state-of-the-art deep learning empowered generative models that are
trained based on the principle of learning forward and reverse diffusion processes via …

Effective offline RL methods require properly handling out-of-distribution actions. Implicit Q-
learning (IQL) addresses this by training a Q-function using only dataset actions through a …

Guided sampling is a vital approach for applying diffusion models in real-world tasks that
embeds human-defined guidance during the sampling procedure. This paper considers a …

Abstract This paper introduces Elastic Decision Transformer (EDT), a significant
advancement over the existing Decision Transformer (DT) and its variants. Although DT …

Self-adaptive systems (SASs) are designed to handle changes and uncertainties through a
feedback loop with four core functionalities: monitoring, analyzing, planning, and execution …

Diffusion models surpass previous generative models in sample quality and training
stability. Recent works have shown the advantages of diffusion models in improving …

Policy constraint methods to offline reinforcement learning (RL) typically utilize
parameterization or regularization that constrains the policy to perform actions within the …

Abstract Diffusion Models (DMs) are state-of-the-art generative models that learn a
reversible corruption process from iterative noise addition and denoising. They are the …