Offline Guarded Safe Reinforcement Learning for Medical Treatment Optimization Strategies

• URL: http://arxiv.org/abs/2505.16242v1
• Date: Thu, 22 May 2025 05:22:03 GMT
• Title: Offline Guarded Safe Reinforcement Learning for Medical Treatment Optimization Strategies
• Authors: Runze Yan, Xun Shen, Akifumi Wachi, Sebastien Gros, Anni Zhao, Xiao Hu,
• Abstract summary: Out-of-distribution (OOD) issues pose significant risks when applying offline reinforcement learning (RL) in healthcare scenarios.<n>We propose textitOffline Guarded Safe Reinforcement Learning ($mathsfOGSRL$), a theoretically grounded model-based offline RL framework.
• Score: 9.123989363028945
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: When applying offline reinforcement learning (RL) in healthcare scenarios, the out-of-distribution (OOD) issues pose significant risks, as inappropriate generalization beyond clinical expertise can result in potentially harmful recommendations. While existing methods like conservative Q-learning (CQL) attempt to address the OOD issue, their effectiveness is limited by only constraining action selection by suppressing uncertain actions. This action-only regularization imitates clinician actions that prioritize short-term rewards, but it fails to regulate downstream state trajectories, thereby limiting the discovery of improved long-term treatment strategies. To safely improve policy beyond clinician recommendations while ensuring that state-action trajectories remain in-distribution, we propose \textit{Offline Guarded Safe Reinforcement Learning} ($\mathsf{OGSRL}$), a theoretically grounded model-based offline RL framework. $\mathsf{OGSRL}$ introduces a novel dual constraint mechanism for improving policy with reliability and safety. First, the OOD guardian is established to specify clinically validated regions for safe policy exploration. By constraining optimization within these regions, it enables the reliable exploration of treatment strategies that outperform clinician behavior by leveraging the full patient state history, without drifting into unsupported state-action trajectories. Second, we introduce a safety cost constraint that encodes medical knowledge about physiological safety boundaries, providing domain-specific safeguards even in areas where training data might contain potentially unsafe interventions. Notably, we provide theoretical guarantees on safety and near-optimality: policies that satisfy these constraints remain in safe and reliable regions and achieve performance close to the best possible policy supported by the data.

Related papers

• Probabilistic Shielding for Safe Reinforcement Learning [51.35559820893218]
  In real-life scenarios, a Reinforcement Learning (RL) agent must often also behave in a safe manner, including at training time.<n>We present a new, scalable method, which enjoys strict formal guarantees for Safe RL.<n>We show that our approach provides a strict formal safety guarantee that the agent stays safe at training and test time.
  arXiv  Detail & Related papers  (2025-03-09T17:54:33Z)
• Safe Reinforcement Learning using Finite-Horizon Gradient-based Estimation [25.552241659930445]
  Key aspect of Safe Reinforcement Learning (Safe RL) involves estimating the constraint condition for the next policy.<n>Existing estimation methods rely on the infinite-horizon discounted advantage function.<n>We propose the first estimation method for finite-horizon non-discounted constraints in deep Safe RL.
  arXiv  Detail & Related papers  (2024-12-15T10:05:23Z)
• FAWAC: Feasibility Informed Advantage Weighted Regression for Persistent Safety in Offline Reinforcement Learning [7.888219789657414]
  Safe offline reinforcement learning aims to learn policies that maximize cumulative rewards while adhering to safety constraints.<n>Key challenge is balancing safety and performance, particularly when the policy encounters out-of-distribution states and actions.<n>We introduce Feasibility Informed Advantage Weighted Actor-Critic (FAWAC), a method that prioritizes persistent safety in constrained Markov decision processes.
  arXiv  Detail & Related papers  (2024-12-12T02:28:50Z)
• Latent Safety-Constrained Policy Approach for Safe Offline Reinforcement Learning [7.888219789657414]
  In safe offline reinforcement learning (RL), the objective is to develop a policy that maximizes cumulative rewards while strictly adhering to safety constraints.<n>We address these issues with a novel approach that begins by learning a conservatively safe policy through the use of Conditional Variational Autoencoders.<n>We frame this as a Constrained Reward-Return Maximization problem, wherein the policy aims to optimize rewards while complying with the inferred latent safety constraints.
  arXiv  Detail & Related papers  (2024-12-11T22:00:07Z)
• Embedding Safety into RL: A New Take on Trust Region Methods [1.5733417396701983]
  We introduce Constrained Trust Region Policy Optimization (C-TRPO), which reshapes policy space to ensure trust regions contain only safe policies.<n>Experiments show that C-TRPO reduces constraint violations while maintaining competitive returns.
  arXiv  Detail & Related papers  (2024-11-05T09:55:50Z)
• Pruning the Way to Reliable Policies: A Multi-Objective Deep Q-Learning Approach to Critical Care [46.2482873419289]
  We introduce a deep Q-learning approach to obtain more reliable critical care policies. We evaluate our method in off-policy and offline settings using simulated environments and real health records from intensive care units.
  arXiv  Detail & Related papers  (2023-06-13T18:02:57Z)
• Deep Offline Reinforcement Learning for Real-world Treatment Optimization Applications [3.770564448216192]
  We introduce a practical and theoretically grounded transition sampling approach to address action imbalance during offline RL training. We perform extensive experiments on two real-world tasks for diabetes and sepsis treatment optimization. Across a range of principled and clinically relevant metrics, we show that our proposed approach enables substantial improvements in expected health outcomes.
  arXiv  Detail & Related papers  (2023-02-15T09:30:57Z)
• Safety Correction from Baseline: Towards the Risk-aware Policy in Robotics via Dual-agent Reinforcement Learning [64.11013095004786]
  We propose a dual-agent safe reinforcement learning strategy consisting of a baseline and a safe agent. Such a decoupled framework enables high flexibility, data efficiency and risk-awareness for RL-based control. The proposed method outperforms the state-of-the-art safe RL algorithms on difficult robot locomotion and manipulation tasks.
  arXiv  Detail & Related papers  (2022-12-14T03:11:25Z)
• Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environments [84.3830478851369]
  We propose a safe reinforcement learning approach that can jointly learn the environment and optimize the control policy. Our approach can effectively enforce hard safety constraints and significantly outperform CMDP-based baseline methods in system safe rate measured via simulations.
  arXiv  Detail & Related papers  (2022-09-29T20:49:25Z)
• Safe Reinforcement Learning via Confidence-Based Filters [78.39359694273575]
  We develop a control-theoretic approach for certifying state safety constraints for nominal policies learned via standard reinforcement learning techniques. We provide formal safety guarantees, and empirically demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2022-07-04T11:43:23Z)
• Conservative Safety Critics for Exploration [120.73241848565449]
  We study the problem of safe exploration in reinforcement learning (RL) We learn a conservative safety estimate of environment states through a critic. We show that the proposed approach can achieve competitive task performance while incurring significantly lower catastrophic failure rates.
  arXiv  Detail & Related papers  (2020-10-27T17:54:25Z)
• Cautious Reinforcement Learning with Logical Constraints [78.96597639789279]
  An adaptive safe padding forces Reinforcement Learning (RL) to synthesise optimal control policies while ensuring safety during the learning process. Theoretical guarantees are available on the optimality of the synthesised policies and on the convergence of the learning algorithm.
  arXiv  Detail & Related papers  (2020-02-26T00:01:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.