ICU-Sepsis: A Benchmark MDP Built from Real Medical Data
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Abstract (arXiv)
We present ICU-Sepsis, an environment that can be used in benchmarks for evaluating reinforcement learning (RL) algorithms. Sepsis management is a complex task that has been an important topic in applied RL research in recent years. Therefore, MDPs that model sepsis management can serve as part of a benchmark to evaluate RL algorithms on a challenging real-world problem. However, creating usable MDPs that simulate sepsis care in the ICU remains a challenge due to the complexities involved in acquiring and processing patient data. ICU-Sepsis is a lightweight environment that models personalized care of sepsis patients in the ICU. The environment is a tabular MDP that is widely compatible and is challenging even for state-of-the-art RL algorithms, making it a valuable tool for benchmarking their performance. However, we emphasize that while ICU-Sepsis provides a standardized environment for evaluating RL algorithms, it should not be used to draw conclusions that guide medical practice.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 8 of its 40 references that are in the map. First posted on arXiv 9 Jun 2024.
Builds on
8 of 40 references.
References · 8
- 2017Proximal Policy Optimization Algorithmscited by 14,441
- 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
- 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
- 2019Reinforcement Learning in Healthcare: A Surveycited by 308
- 2019Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Modelscited by 114
- 2017Deep Reinforcement Learning for Sepsis Treatmentcited by 71
- 2021Unifying Cardiovascular Modelling with Deep Reinforcement Learning for Uncertainty Aware Control of Sepsis Treatmentcited by 16
- 2020Identifying Distinct, Effective Treatments for Acute Hypotension with SODA-RL: Safely Optimized Diverse Accurate Reinforcement Learningcited by 4
Built on by
Citing papers per year: 2024: 2; 2025: 2; 2026: 2.
6 papers.
- 2024Gymnasium: A Standard Interface for Reinforcement Learning Environmentscited by 35
- 2025Exploring Time-Step Size in Reinforcement Learning for Sepsis Treatmentcited by 4
- 2026Formally Verifying and Explaining Sepsis Treatment Policies with COOL-MCcited by 3
- 2025MDPs with a State Sensing Costcited by 1
- 2024Bayesian Inverse Transition Learning: Learning Dynamics From Near-Optimal Trajectoriescited by 0
- 2026A Unified Benchmark for Dynamic Medical Treatment Reinforcement Learningcited by 0
- 2026A Unified Benchmark for Dynamic Medical Treatment Reinforcement Learning31 May 2026
- 2026Formally Verifying and Explaining Sepsis Treatment Policies with COOL-MC16 Feb 2026
- 2025Exploring Time-Step Size in Reinforcement Learning for Sepsis Treatment25 Nov 2025
- 2025MDPs with a State Sensing Cost6 May 2025
- 2024Bayesian Inverse Transition Learning: Learning Dynamics From Near-Optimal Trajectories7 Nov 2024
- 2024Gymnasium: A Standard Interface for Reinforcement Learning Environments24 Jul 2024