mlsanityData to 7 Oct 2026

DTR-Bench: An in silico Environment and Benchmark Platform for Reinforcement Learning Based Dynamic Treatment Regime

arXiv 2405.18610 ↗First posted 28 May 2024cs.LG cs.AIcited by 8

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Abstract (arXiv)

Reinforcement learning (RL) has garnered increasing recognition for its potential to optimise dynamic treatment regimes (DTRs) in personalised medicine, particularly for drug dosage prescriptions and medication recommendations. However, a significant challenge persists: the absence of a unified framework for simulating diverse healthcare scenarios and a comprehensive analysis to benchmark the effectiveness of RL algorithms within these contexts. To address this gap, we introduce textitDTR-Bench, a benchmarking platform comprising four distinct simulation environments tailored to common DTR applications, including cancer chemotherapy, radiotherapy, glucose management in diabetes, and sepsis treatment. We evaluate various state-of-the-art RL algorithms across these settings, particularly highlighting their performance amidst real-world challenges such as pharmacokinetic/pharmacodynamic (PK/PD) variability, noise, and missing data. Our experiments reveal varying degrees of performance degradation among RL algorithms in the presence of noise and patient variability, with some algorithms failing to converge. Additionally, we observe that using temporal observation representations does not consistently lead to improved performance in DTR settings. Our findings underscore the necessity of developing robust, adaptive RL algorithms capable of effectively managing these complexities to enhance patient-specific healthcare. We have open-sourced our benchmark and code at https://github.com/GilesLuo/DTR-Bench.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 21 of its 51 references that are in the map. First posted on arXiv 28 May 2024.

Builds on

21 of 51 references.

References · 21

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9 more
  1. 2019Empirical Study of Off-Policy Policy Evaluation for Reinforcement Learningcited by 121
  2. 2019Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Modelscited by 114
  3. 2022A Review of Off-Policy Evaluation in Reinforcement Learningcited by 92
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  6. 2023Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcarecited by 41
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  8. 2020Basal Glucose Control in Type 1 Diabetes using Deep Reinforcement Learning: An In Silico Validationcited by 18
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 5.

8 papers.

  1. 2025medDreamer: Model-Based Reinforcement Learning with Latent Imagination on Complex EHRs for Clinical Decision Supportcited by 8
  2. 2024Offline Inverse Constrained Reinforcement Learning for Safe-Critical Decision Making in Healthcarecited by 4
  3. 2025Are Large Language Models Dynamic Treatment Planners? An In Silico Study from a Prior Knowledge Injection Anglecited by 1
  4. 2026ALPACA: A Reinforcement Learning Environment for Medication Repurposing and Treatment Optimization in Alzheimer's Diseasecited by 0
  5. 2026Hyperparameter Trajectory Inference with Conditional Lagrangian Optimal Transportcited by 0
  6. 2026Recurrent Deep Reinforcement Learning for Chemotherapy Control under Partial Observabilitycited by 0
  7. 2026A Unified Benchmark for Dynamic Medical Treatment Reinforcement Learningcited by 0
  8. 2026DT2: Decision-Targeted Digital Twinscited by 0