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

Diffusion models, praised for their success in generative tasks, are increasingly being
applied to robotics, demonstrating exceptional performance in behavior cloning. However …

Foundation models (FMs), large deep learning models pre-trained on vast, unlabeled
datasets, exhibit powerful capabilities in understanding complex patterns and generating …

Constrained policy search (CPS) is a fundamental problem in offline reinforcement learning,
which is generally solved by advantage weighted regression (AWR). However, previous …

Drawing upon recent advances in language model alignment, we formulate offline
Reinforcement Learning as a two-stage optimization problem: First pretraining expressive …

Diffusion-based models have achieved notable empirical successes in reinforcement
learning (RL) due to their expressiveness in modeling complex distributions. Despite …

Applying diffusion models in reinforcement learning for long-term planning has gained much
attention recently. Several diffusion-based methods have successfully leveraged the …

The Mean Square Error (MSE) is commonly utilized to estimate the solution of the optimal
value function in the vast majority of offline reinforcement learning (RL) models and has …

Offline reinforcement learning (RL) leverages pre-collected datasets to train optimal policies.
Diffusion Q-Learning (DQL), introducing diffusion models as a powerful and expressive …