EasyRL4Rec: A User-Friendly Code Library for Reinforcement Learning
  Based Recommender Systems
Yuanqing Yu Chongming Gao Jiawei Chen Heng Tang Yuefeng Sun Qian Chen Weizhi Ma Min Zhang
Reinforcement Learning (RL)-Based Recommender Systems (RSs) are increasingly
recognized for their ability to improve long-term user engagement. Yet, the
field grapples with challenges such as the absence of accessible frameworks,
inconsistent evaluation standards, and the complexity of replicating prior
work. Addressing these obstacles, we present EasyRL4Rec, a user-friendly and
efficient library tailored for RL-based RSs. EasyRL4Rec features lightweight,
diverse RL environments built on five widely-used public datasets, and is
equipped with comprehensive core modules that offer rich options to ease the
development of models. It establishes consistent evaluation criteria with a
focus on long-term impacts and introduces customized solutions for state
modeling and action representation tailored to recommender systems.
Additionally, we share valuable insights gained from extensive experiments with
current methods. EasyRL4Rec aims to facilitate the model development and
experimental process in the domain of RL-based RSs. The library is openly
accessible at https://github.com/chongminggao/EasyRL4Rec.