通过观测映射和行为克隆进行少样本策略迁移的框架
A Framework for Few-Shot Policy Transfer through Observation Mapping and Behavior Cloning
AI总结：
提出一种基于观测映射和行为克隆的少样本策略迁移框架，利用GAN和循环一致性损失实现跨域策略迁移，在有限交互及语义差异下仍有效。
摘要：
尽管强化学习在机器人应用方面最近取得了进展，但由于昂贵的交互成本，许多任务仍然难以解决。迁移学习通过转移在源域中学到的知识，有助于减少目标域中的训练时间。Sim2Real迁移有助于将知识从模拟机器人域转移到物理目标域。知识转移减少了在物理世界中训练任务所需的时间，因为物理世界中的交互成本很高。然而，大多数现有方法假设两个域在任务结构和物理属性上完全对应。这项工作提出了一个通过观测映射和行为克隆在两个域之间进行少样本策略迁移的框架。我们使用生成对抗网络（GANs）结合循环一致性损失来映射源域和目标域之间的观测，然后利用这种学习到的映射将成功的源任务行为策略克隆到目标域。我们观察到，在有限的目标任务交互以及源任务和目标任务在语义上不相似的情况下，行为策略迁移是成功的。
英文摘要：
Despite recent progress in Reinforcement Learning for robotics applications, many tasks remain prohibitively difficult to solve because of the expensive interaction cost. Transfer learning helps reduce the training time in the target domain by transferring knowledge learned in a source domain. Sim2Real transfer helps transfer knowledge from a simulated robotic domain to a physical target domain. Knowledge transfer reduces the time required to train a task in the physical world, where the cost of interactions is high. However, most existing approaches assume exact correspondence in the task structure and the physical properties of the two domains. This work proposes a framework for Few-Shot Policy Transfer between two domains through Observation Mapping and Behavior Cloning. We use Generative Adversarial Networks (GANs) along with a cycle-consistency loss to map the observations between the source and target domains and later use this learned mapping to clone the successful source task behavior policy to the target domain. We observe successful behavior policy transfer with limited target task interactions and in cases where the source and target task are semantically dissimilar.
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。
