Efficient and Uncertainty-Aware Diffusion Framework for Offline-to-Online Reinforcement Learning
arXiv:2605.30776v1 Announce Type: new Abstract: Offline-to-Online Reinforcement Learning (O2O-RL) leverages an offline, pre-trained policy to minimize costly online interactions. Although data-efficient, O2O-RL is susceptible to shifts between offline and online distributions. Existing work aims to mitigate the harm of this shift by finetuning the policy on trajectory data sampled from a diffusion model. Inspired by this line of work, we propose DUAL: an efficient \textbf{D}iffusion \textbf{U}ncertainty-\textbf{A}ware framework for offline-to-online reinforcement \textbf{L}earning. DUAL utiliz
The continuous drive to improve AI efficiency and robustness in real-world applications, especially in reinforcement learning, is leading to innovations like DUAL.
This research addresses a key challenge in moving AI from controlled environments to practical use, making autonomous systems more reliable and cost-effective to deploy.
The proposed DUAL framework offers a more efficient and uncertainty-aware method for applying offline-trained reinforcement learning policies to online scenarios.
- · AI developers
- · Robotics companies
- · Logistics and automation sectors
- · Inefficient reinforcement learning models
- · Companies relying on extensive online data collection
Improved performance and reduced data requirements for deploying AI agents in dynamic environments.
Accelerated development and adoption of AI-driven autonomous systems across various industries due to lower operational costs.
Increased societal integration of AI, leading to new ethical and regulatory challenges regarding autonomous decision-making.
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