Beyond deep reinforcement learning: A tutorial on generative diffusion models in network optimization
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 …
Generative Artificial Intelligence (GAI), demonstrating their versatility and efficacy across a …
Diffusion model is an effective planner and data synthesizer for multi-task reinforcement learning
Diffusion models have demonstrated highly-expressive generative capabilities in vision and
NLP. Recent studies in reinforcement learning (RL) have shown that diffusion models are …
NLP. Recent studies in reinforcement learning (RL) have shown that diffusion models are …
Opportunities and challenges of diffusion models for generative AI
Diffusion models, a powerful and universal generative artificial intelligence technology, have
achieved tremendous success and opened up new possibilities in diverse applications. In …
achieved tremendous success and opened up new possibilities in diverse applications. In …
Elastic decision transformer
Abstract This paper introduces Elastic Decision Transformer (EDT), a significant
advancement over the existing Decision Transformer (DT) and its variants. Although DT …
advancement over the existing Decision Transformer (DT) and its variants. Although DT …
Reward-directed conditional diffusion: Provable distribution estimation and reward improvement
We explore the methodology and theory of reward-directed generation via conditional
diffusion models. Directed generation aims to generate samples with desired properties as …
diffusion models. Directed generation aims to generate samples with desired properties as …
Diffusion models for reinforcement learning: A survey
Diffusion models surpass previous generative models in sample quality and training
stability. Recent works have shown the advantages of diffusion models in improving …
stability. Recent works have shown the advantages of diffusion models in improving …
Skilldiffuser: Interpretable hierarchical planning via skill abstractions in diffusion-based task execution
Diffusion models have demonstrated strong potential for robotic trajectory planning.
However generating coherent trajectories from high-level instructions remains challenging …
However generating coherent trajectories from high-level instructions remains challenging …
Diffusion policy policy optimization
We introduce Diffusion Policy Policy Optimization, DPPO, an algorithmic framework
including best practices for fine-tuning diffusion-based policies (eg Diffusion Policy) in …
including best practices for fine-tuning diffusion-based policies (eg Diffusion Policy) in …
Sparse diffusion policy: A sparse, reusable, and flexible policy for robot learning
The increasing complexity of tasks in robotics demands efficient strategies for multitask and
continual learning. Traditional models typically rely on a universal policy for all tasks, facing …
continual learning. Traditional models typically rely on a universal policy for all tasks, facing …
Edmp: Ensemble-of-costs-guided diffusion for motion planning
Classical motion planning for robotic manipulation includes a set of general algorithms that
aim to minimize a scene-specific cost of executing a given plan. This approach offers …
aim to minimize a scene-specific cost of executing a given plan. This approach offers …