# Retentive Decision Transformer with Adaptive Masking for Reinforcement Learning based Recommendation Systems

arXiv: https://arxiv.org/abs/2403.17634 · HTML: https://mlsanity.com/paper/2403.17634/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Decision transformers and sequence modeling, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 27 of its 44 references that are in the map.
- First posted on arXiv 26 Mar 2024.

## Placed in

- Offline RL and imitation learning from data / [Decision transformers and sequence modeling](https://mlsanity.com/offlinerl/decision-transformers-and-sequence-modeling/)
- Deep reinforcement learning methods / [RL training of language and agent models](https://mlsanity.com/deeprl/rl-training-of-language-and-agent-models/)

## Builds on (most cited first, 27 of 27)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://mlsanity.com/paper/2106.01345/) (2021)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [RWKV: Reinventing RNNs for the Transformer Era](https://mlsanity.com/paper/2305.13048/) (2023)
- [Fast Transformer Decoding: One Write-Head is All You Need](https://mlsanity.com/paper/1911.02150/) (2019)
- [Offline Reinforcement Learning as One Big Sequence Modeling Problem](https://mlsanity.com/paper/2106.02039/) (2021)
- [S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization](https://mlsanity.com/paper/2008.07873/) (2020)
- [Reinforcement learning based recommender systems: A survey](https://mlsanity.com/paper/2101.06286/) (2021)
- [Behavior Sequence Transformer for E-commerce Recommendation in Alibaba](https://mlsanity.com/paper/1905.06874/) (2019)
- [Reinforcement Knowledge Graph Reasoning for Explainable Recommendation](https://mlsanity.com/paper/1906.05237/) (2019)
- [Deep Reinforcement Learning for Page-wise Recommendations](https://mlsanity.com/paper/1805.02343/) (2018)
- [Virtual-Taobao: Virtualizing Real-world Online Retail Environment for Reinforcement Learning](https://mlsanity.com/paper/1805.10000/) (2018)
- [Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning](https://mlsanity.com/paper/1802.06501/) (2018)
- [Alleviating Matthew Effect of Offline Reinforcement Learning in Interactive Recommendation](https://mlsanity.com/paper/2307.04571/) (2023)
- [Automated Self-Supervised Learning for Recommendation](https://mlsanity.com/paper/2303.07797/) (2023)
- [Model-Based Reinforcement Learning with Adversarial Training for Online Recommendation](https://mlsanity.com/paper/1911.03845/) (2019)
- [Large-scale Interactive Recommendation with Tree-structured Policy Gradient](https://mlsanity.com/paper/1811.05869/) (2018)
- [KuaiSim: A Comprehensive Simulator for Recommender Systems](https://mlsanity.com/paper/2309.12645/) (2023)
- [Knowledge-guided Deep Reinforcement Learning for Interactive Recommendation](https://mlsanity.com/paper/2004.08068/) (2020)
- [User Retention-oriented Recommendation with Decision Transformer](https://mlsanity.com/paper/2303.06347/) (2023)
- [Generative Inverse Deep Reinforcement Learning for Online Recommendation](https://mlsanity.com/paper/2011.02248/) (2020)
- [Transformers in Reinforcement Learning: A Survey](https://mlsanity.com/paper/2307.05979/) (2023)
- [Causal Decision Transformer for Recommender Systems via Offline Reinforcement Learning](https://mlsanity.com/paper/2304.07920/) (2023)

## Built on by (most cited first, 3 of 3)

- [Energy-Guided Diffusion Sampling for Long-Term User Behavior Prediction in Reinforcement Learning-based Recommendation](https://mlsanity.com/paper/2510.12815/) (2025)
- [Adaptive Learning Systems: Personalized Curriculum Design Using LLM-Powered Analytics](https://mlsanity.com/paper/2507.18949/) (2025)
- [Generative Sequential Notification Optimization via Multi-Objective Decision Transformers](https://mlsanity.com/paper/2509.02458/) (2025)

## Abstract (arXiv)

Reinforcement Learning-based Recommender Systems (RLRS) have shown promise across a spectrum of applications, from e-commerce platforms to streaming services. Yet, they grapple with challenges, notably in crafting reward functions and harnessing large pre-existing datasets within the RL framework. Recent advancements in offline RLRS provide a solution for how to address these two challenges. However, existing methods mainly rely on the transformer architecture, which, as sequence lengths increase, can introduce challenges associated with computational resources and training costs. Additionally, the prevalent methods employ fixed-length input trajectories, restricting their capacity to capture evolving user preferences. In this study, we introduce a new offline RLRS method to deal with the above problems. We reinterpret the RLRS challenge by modeling sequential decision-making as an inference task, leveraging adaptive masking configurations. This adaptive approach selectively masks input tokens, transforming the recommendation task into an inference challenge based on varying token subsets, thereby enhancing the agent's ability to infer across diverse trajectory lengths. Furthermore, we incorporate a multi-scale segmented retention mechanism that facilitates efficient modeling of long sequences, significantly enhancing computational efficiency. Our experimental analysis, conducted on both online simulator and offline datasets, clearly demonstrates the advantages of our proposed method.