Decision Transformer

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Decision Transformers treat reinforcement learning as sequence modeling: a transformer predicts actions from past states, actions, and a desired return-to-go, often using fixed offline datasets rather than online exploration. Work explores more efficient conditioning and trajectory sampling, critic-guided action selection, prompts and mixture-of-experts for multitask policies, and online fine-tuning. Evaluation spans D4RL locomotion and navigation, robotic manipulation, and applied settings such as wireless control, auctions, and combinatorial optimization. A central challenge is making desired returns reliably control performance when data are limited or low quality, while keeping policies robust to unfamiliar states and long-term dependencies.

Latest papers

19 papers2 papers in the last four weeks, down 33% on the four weeks before. 0.0% of all new papers.

ClinSDT: LLM-Encoded Clinical Semantic Guidance for Sepsis Treatment via Decision Transformer
Oct 18, 2026Yanyan Wu, Guanyu Zhu, Changbin Zhao +7Healthcare Clinical Decision-Making
South China Agricultural University, Guangzhou, China · The Ohio State University, Columbus, Ohio, United States
Decision Titan: Test-Time Training for Long-Term Memory in Offline Reinforcement Learning
Oct 1, 2026Jude Waide, Robert LieckReinforcement Learning Transformer-Based RL
Department of Computer Science, University of Durham, Durham, United Kingdom.
Trust Guided Decision Transformer
Sep 25, 2026Chainesh Gautam, Raghuram Bharadwaj Diddigi, Chandramouli Kamanchi +3Transformer-Based RL Decision Transformer
International Institute of Information Technology Bangalore · IBM Research Bangalore
The Operable Pareto Front: Distilling Offline Search into Run-Time Control for Multi-Objective UAV Edge-Computing Scheduling
Sep 16, 2026Qiao Liao, Zhiyong Feng, Bin Wu +1Trajectory Optimization Edge Computing
School of Computer Science and Technology, Tianjin University, Tianjin, China · School of Computer Software, Tianjin University, Tianjin, China · School of Information Science and Engineering, Shandong Agriculture and Engineering University, Shandong, China
WiSDoM: Wireless Sparse Decision Transformer with Mixture-of-Experts for Multi-Task Mobile Network Optimization
Aug 31, 2026Fatih Temiz, Shavbo Salehi, Melike Erol-KantarciSparse Mixture-of-Experts Decision Transformer
School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada
Hybrid Offline-Online Multi-Agent Decision Transformers for Wireless Resource Management
Aug 28, 2026Yiming Zhang, Kun Yang, Cong Shen +1Wireless Resource Allocation Decentralized MARL
Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL 60208 USA · Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA 22904 USA
Guided Riemannian Optimization (GuRO): Bridging Model Predictive Control and Decision Transformers
Aug 24, 2026Hossein Abdi, Satya Prakash Dash, Mingfei SunRL Control Transformer-Based RL
Department of Computer Science, The University of Manchester, Manchester, UK
Towards Scalable Multi-Task Reinforcement Learning with Large Decision Models
Jun 23, 2026Thibaut KulakReinforcement Learning Transformer
NeoInstinct SA
OnDeFog: Online Decision Transformer under Frame Dropping
Jun 18, 2026Daiki Yotsufuji, Kenta Nishihara, Shoma Shimizu +2Reinforcement Learning Transformer
Yokohama National University, Kanagawa, Japan
Generalizable Multi-Task Learning for Wireless Networks Using Prompt Decision Transformers
Jun 3, 2026Fatih Temiz, Shavbo Salehi, Melike Erol-KantarciReinforcement Learning Transformer
School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada
Return-to-Go Is More Than a Number: Q-Guided Alignment for Return-Conditioned Supervised Learning
May 27, 2026Yuxiao Yang, Weitong ZhangReinforcement Learning Decision Transformer
University of North Carolina at Chapel Hill
Generative Auto-Bidding with Unified Modeling and Exploration
May 19, 2026Mingming Zhang, Feiqing Zhuang, Na Li +7Automated Bidding Online Advertising
Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University · Taobao & Tmall Group of Alibaba
Beyond Autoregressive RTG: Conditioning via Injection Outside Sequential Modeling in Decision Transformer
May 7, 2026Yongyi Wang, Hanyu Liu, Lingfeng Li +6Efficient Transformer Inference Reinforcement Learning
School of Computer Science Peking University Beijing, China 100871
E $^2$ DT: Efficient and Effective Decision Transformer with Experience-Aware Sampling for Robotic Manipulation
Apr 30, 2026Kaiyan Zhao, Borong Zhang, Yiming Wang +4Long-Horizon Robotic Manipulation Transformer
School of Computer Science, Wuhan University, Wuhan 430072, China · State Key Lab of Internet of Things for Smart City and Department of Computer Information Science, University of Macau, Macao 999078, China
Offline Decision Transformers for Neural Combinatorial Optimization: Surpassing Heuristics on the Traveling Salesman Problem
Mar 26, 2026Hironori Ohigashi, Shinichiro HamadaPointer Networks Neural Combinatorial Optimization
Panasonic Connect Co., Ltd., Japan
Emergency Preemption Without Online Exploration: A Decision Transformer Approach
Mar 20, 2026Haoran Su, Hanxiao Deng, Yandong SunTraffic Signal Control Decision Transformer
New York University · University of California, Berkeley
Selecting Offline Reinforcement Learning Algorithms for Stochastic Network Control
Mar 4, 2026Nicolas Helson, Pegah Alizadeh, Anastasios GiovanidisConservative RL Offline RL
Ericsson Research, Massy, France
Edge Learning via Federated Split Decision Transformers for Metaverse Resource Allocation
Feb 18, 2026Fatih Temiz, Shavbo Salehi, Melike Erol-KantarciWireless Resource Allocation Federated RL
School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada
Direct Regret Optimization in Bayesian Optimization
Jul 9, 2025Fengxue Zhang, Yuxin ChenBayesian Optimization Learning to Optimize
Department of Computer Science University of Chicago Chicago, IL 60637

Oct 18, 2026J/K move · Enter open · S save

ClinSDT: LLM-Encoded Clinical Semantic Guidance for Sepsis Treatment via Decision Transformer

Yanyan Wu, Guanyu Zhu, Changbin Zhao, Jin Zhang+6

South China Agricultural University, Guangzhou, China · The Ohio State University, Columbus, Ohio, United States

Healthcare Clinical Decision-Making Offline RL LLM-Guided RL Decision Transformer

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Offline sepsis treatment aims to learn treatment policies from retrospective ICU trajectories. Decision Transformer-based methods provide a natural sequence modeling framework for this task, but they mainly rely on raw physiological states and historical clinician actions. Such raw trajectory tokens may not explicitly capture clinical semantics that clinicians use in practice, such as patient severity, recent treatment response, and current risk priorities. To address this limitation, we propose ClinSDT, an LLM-assisted Decision Transformer framework for offline sepsis treatment. ClinSDT uses a frozen LLM as a semantic bridge rather than a treatment decision maker. It constructs three clinical semantic views from ICU trajectories, namely Profile, Context, and Advisory, and inserts their encoded representations as independent tokens into a Decision Transformer policy. To support policy refinement and rollout, ClinSDT further trains a Treatment-Response Forecaster to estimate the next physiological state, SAPS-II change, and predictive uncertainty from state-action inputs. In the second training stage, the forecaster screens local counterfactual treatment candidates, and only low-uncertainty candidates with lower predicted SAPS-II increase are distilled into the policy after semantic regeneration. During inference, ClinSDT performs closed-loop treatment rollout by refreshing semantic inputs from the currently available patient state. Experiments on MIMIC-III and MIMIC-IV show that ClinSDT achieves consistent relative improvements over representative imitation learning, offline reinforcement learning, and Decision Transformer baselines under the same rollout protocol. Code and data are available at https://github.com/KAG778/ClinSDT.