Reinforcement Learning (RL)-Based Recommender Systems (RSs) have gained rising attention for their potential to enhance long-term user engagement. However, research in this field faces challenges, including the lack of user-friendly frameworks, inconsistent evaluation metrics, and difficulties in reproducing existing studies. To tackle these issues, we introduce EasyRL4Rec, an easy-to-use code library designed specifically for RL-based RSs. This library provides lightweight and diverse RL environments based on five public datasets and includes core modules with rich options, simplifying model development. It provides unified evaluation standards focusing on long-term outcomes and offers tailored designs for state modeling and action representation for recommendation scenarios. Furthermore, we share our findings from insightful experiments with current methods. EasyRL4Rec seeks to facilitate the model development and experimental process in the domain of RL-based RSs. The library is available for public use.

翻译：基于强化学习的推荐系统因能提升长期用户参与度而日益受到关注。然而，该领域研究面临缺乏用户友好框架、评估指标不一致以及现有研究复现困难等挑战。为解决这些问题，我们推出了EasyRL4Rec——一个专为基于强化学习的推荐系统设计的易用代码库。该库基于五个公开数据集提供轻量级且多样化的强化学习环境，并包含功能丰富的核心模块，可简化模型开发流程。通过聚焦长期效果的统一评估标准，以及针对推荐场景中状态建模和动作表示的特化设计，本库为研究者提供了标准化的实验支持。此外，我们分享了当前方法在深入实验中的发现。EasyRL4Rec旨在促进基于强化学习的推荐系统领域的模型开发与实验过程。该库现已开放供公众使用。