Autonomous agents operating in domains such as robotics or video game simulations must adapt to changing tasks without forgetting about the previous ones. This process called Continual Reinforcement Learning poses non-trivial difficulties, from preventing catastrophic forgetting to ensuring the scalability of the approaches considered. Building on recent advances, we introduce a benchmark providing a suite of video-game navigation scenarios, thus filling a gap in the literature and capturing key challenges : catastrophic forgetting, task adaptation, and memory efficiency. We define a set of various tasks and datasets, evaluation protocols, and metrics to assess the performance of algorithms, including state-of-the-art baselines. Our benchmark is designed not only to foster reproducible research and to accelerate progress in continual reinforcement learning for gaming, but also to provide a reproducible framework for production pipelines -- helping practitioners to identify and to apply effective approaches.

翻译：在机器人或视频游戏模拟等领域中运行的自主智能体必须适应不断变化的任务，同时不遗忘先前习得的能力。这一被称为持续强化学习的过程带来了诸多非平凡挑战，包括防止灾难性遗忘、确保所采用方法的可扩展性等。基于最新研究进展，我们提出了一个包含系列视频游戏导航场景的基准测试套件，从而填补了现有研究空白并捕捉了以下核心挑战：灾难性遗忘、任务适应性和内存效率。我们定义了一系列多样化任务与数据集、评估协议及性能指标，用以评估包括最先进基线算法在内的各类算法表现。本基准测试不仅旨在促进可复现研究、加速游戏领域持续强化学习的进展，同时为生产流水线提供了可复现框架——帮助从业者识别并应用有效的解决方案。