Ray/RLLib-Empowered Reinforcement Learning Based Recommender Systems in NetEase Game

Ray, including Ray/RLLib, has been actually speeding up many reinforce-learning-based services in NetEase Games. It has greatly promoted the user experience of game players and the general profit of various game products as well. We deployed our first reinforcement learning based recommendation system application using ray RLLib. Reinforcement learning is a promising direction since the RL paradigm is inherently suitable for tackling multi-step decision-making problems, optimizing long-term user satisfaction directly, and exploring the combination spaces efficiently. As a way of giving back to the community, we open-sourced the RL4RS (Reinforcement Learning for Recommender Systems) dataset - a new resource fully collected from industrial applications to train and evaluate RL algorithms with special concerns on the reality gaps, contains two real-world datasets, data understanding tools, tuned simulation environments, related advanced RL baselines, batch RL baselines, and counterfactual policy evaluation algorithms. The RL4RS suit can be found at this repo (https://github.com/fuxiAIlab/RL4RS).