论文标题
统一的国家代表性学习在数据增强下
Unified State Representation Learning under Data Augmentation
论文作者
论文摘要
快速域适应的能力对于增加增强学习（RL）对现实世界问题的适用性很重要。 RL代理的概括对于现实世界中的成功至关重要，但是零拍的政策转移是一个具有挑战性的问题，因为即使是小小的视觉变化也可能使训练有素的代理在新任务中完全失败。我们提出了USRA：在数据增强下的统一状态表示学习，这是一个代表学习框架，该框架通过对其观察结果进行数据增强来学习潜在的统一状态表示，以提高其推广到看不见的目标域的能力。我们在Walker环境中展示了我们方法在DeepMind控制概括基准上的成功，并发现USRA可实现较高的样本效率，而与最佳基线结果相比，USRA可以提高样本效率和14.3％的适应性性能。
The capacity for rapid domain adaptation is important to increasing the applicability of reinforcement learning (RL) to real world problems. Generalization of RL agents is critical to success in the real world, yet zero-shot policy transfer is a challenging problem since even minor visual changes could make the trained agent completely fail in the new task. We propose USRA: Unified State Representation Learning under Data Augmentation, a representation learning framework that learns a latent unified state representation by performing data augmentations on its observations to improve its ability to generalize to unseen target domains. We showcase the success of our approach on the DeepMind Control Generalization Benchmark for the Walker environment and find that USRA achieves higher sample efficiency and 14.3% better domain adaptation performance compared to the best baseline results.
