DIDA：基于域适应的去噪模仿学习
DIDA: Denoised Imitation Learning based on Domain Adaptation
AI总结：
针对从含噪演示中学习（LND）时传统模仿学习方法鲁棒性不足的问题，本文提出DIDA，通过双判别器区分噪声与专业程度并学习域不变任务表示，在MuJoCo多种噪声任务上优于多数基线。
摘要：
从低质量数据集中模仿技能，例如次优演示和带有干扰物的观测，在现实世界应用中很常见。在本文中，我们关注从含噪演示中学习（Learning from Noisy Demonstrations，LND）问题，其中模仿者需要从在数据收集或传输过程中经常出现噪声的数据中学习。以往的模仿学习（IL）方法通过向纯专家数据注入对抗学习得到的高斯噪声，或利用额外的排序信息，来提升所学策略的鲁棒性，但它们在LND场景下可能失效。为缓解上述问题，我们提出了基于域适应的去噪模仿学习（Denoised Imitation learning based on Domain Adaptation，DIDA），该方法设计两个判别器来区分数据的噪声水平和专业程度，从而促使特征编码器学习与任务相关但与域无关的表示。在MuJoCo上的实验结果表明，DIDA能够成功处理来自含多种类型噪声的演示的具有挑战性的模仿任务，性能优于大多数基线方法。
英文摘要：
Imitating skills from low-quality datasets, such as sub-optimal demonstrations and observations with distractors, is common in real-world applications. In this work, we focus on the problem of Learning from Noisy Demonstrations (LND), where the imitator is required to learn from data with noise that often occurs during the processes of data collection or transmission. Previous IL methods improve the robustness of learned policies by injecting an adversarially learned Gaussian noise into pure expert data or utilizing additional ranking information, but they may fail in the LND setting. To alleviate the above problems, we propose Denoised Imitation learning based on Domain Adaptation (DIDA), which designs two discriminators to distinguish the noise level and expertise level of data, facilitating a feature encoder to learn task-related but domain-agnostic representations. Experiment results on MuJoCo demonstrate that DIDA can successfully handle challenging imitation tasks from demonstrations with various types of noise, outperforming most baseline methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
