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
https://gemcollector.github.io/RL-ViGen/