Diffusion policies as an expressive policy class for offline reinforcement learning[J]. arXiv preprint arXiv:2208.06193, 2022. arxiv.org/pdf/2208.0619 1.离线强化学习的挑战:离线强化学习面临的主要挑战是什么? (ABSTRACT) 离线强化学习面临的主要挑战是在不与环境进行实时交互的情况下,从已经收集的静态数据...
在本文中,作者将强化学习中policy看作了一个Diffusion model(扩散模型), 提出了Diffusion Q-learning(Diffusion-QL)算法。Diffusion-QL利用Condition Diffusion model(条件扩散模型)来表示策略。通过学习动作价值函数,并将最大化动作值的项添加到Condition Diffusion model的训练损失中,从而得到一种寻求接近行为策略的最优...
There is another way: "Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning" by Wang, Z. proposed Diffusion Model as policy-optimization in offline RL, et al. Specifically, Diffusion-QL forms policy as a conditional diffusion model with states as the condition from ...
Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning, ICLR 2023. [paper] [code] Offline Reinforcement Learning via High-fidelity Generative Behavior Modeling, ICLR 2023. [paper] [code] Is Conditional Generative Modeling all you need for Decision-Making?, ICLR 2023. [...
Diffusion policies are conditional diffusion models that learn robot action distributions conditioned on the robot and environment state. They have recently shown to outperform both deterministic and alternative action distribution learning formulations. 3D robot policies use 3D scene feature representations agg...
Codebase for OA-ReactDiff is available as an open-source repository on GitHub for contiguous development,https://github.com/chenruduan/OAReactDiff. A stable version of the code56used in this work is available at Zenodo,https://doi.org/10.5281/zenodo.10054963. ...
Diffusion-QL-DIFFUSION POLICIES AS AN EXPRESSIVE POLICY CLASS FOR OFFLINE REINFORCEMENT LEARNING Motivation offline RL会访问OOD动作,因为这样会导致价值函数估计不准确,故将对性能产生影响。以前缓解该问题的方式包括:① 对策略学习目标进行约束,使其接近行为策略;② 约束价值函数,使其对OOD动作进行低估;③ 引入...
Wang, Zhendong, Jonathan J. Hunt, and Mingyuan Zhou. "Diffusion policies as an expressive policy class for offline reinforcement learning."arXiv preprint arXiv:2208.06193(2022). Motivation 利用Diffusion model 的高拟合能力(expressive)来引导策略与数据集对齐,缓解OOD问题。
Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning Zhendong Wang, Jonathan J Hunt, Mingyuan Zhou arXiv 2022. [Paper] [Github] 12 Oct 2022 Offline Reinforcement Learning via High-Fidelity Generative Behavior Modeling Huayu Chen, Cheng Lu, Chengyang Ying, Hang Su...
Abstract:Offline reinforcement learning (RL) leverages pre-collected datasets to train optimal policies. Diffusion Q-Learning (DQL), introducing diffusion models as a powerful and expressive policy class, significantly boosts the performance of offline RL. However, its reliance on iterative denoising samp...