Visual Reinforcement Learning (Visual RL), coupled with high-dimensional
observations, has consistently confronted the long-standing challenge of
generalization. Despite the focus on algorithms aimed at resolving visual
generalization problems, we argue that the devil is in the existing benchmarks
as they are restricted to isolated tasks and generalization categories,
undermining a comprehensive evaluation of agents' visual generalization
capabilities. To bridge this gap, we introduce RL-ViGen: a novel Reinforcement
Learning Benchmark for Visual Generalization, which contains diverse tasks and
a wide spectrum of generalization types, thereby facilitating the derivation of
more reliable conclusions. Furthermore, RL-ViGen incorporates the latest
generalization visual RL algorithms into a unified framework, under which the
experiment results indicate that no single existing algorithm has prevailed
universally across tasks. Our aspiration is that RL-ViGen will serve as a
catalyst in this area, and lay a foundation for the future creation of
universal visual generalization RL agents suitable for real-world scenarios.
Access to our code and implemented algorithms is provided at
this https URL

Visual Reinforcement Learning 中的 generalization 问题是一个长期存在的挑战，该研究介绍了一个新的用于 Visual Generalization 的 Reinforcement Learning 基准测试框架（RL-ViGen），包含多样的任务和广泛的 generalization 类型，以便更可靠地评估 agent 的可视化 generalization 能力，并发现目前没有单一算法能够在所有任务中普遍占优势，旨在为未来创建适用于实际场景的通用视觉 generalization RL agent 奠定基础。