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Offline Inverse Constrained Reinforcement Learning for Safe-Critical Decision Making in Healthcare

Nan Fang, Guiliang Liu, Wei Gong

TL;DR

The Constraint Transformer (CT) is proposed, which utilizes a causal attention mechanism to incorporate historical decisions and observations into the constraint modeling, while employing a Non-Markovian layer for weighted constraints to capture critical states.

Abstract

Reinforcement Learning (RL) applied in healthcare can lead to unsafe medical decisions and treatment, such as excessive dosages or abrupt changes, often due to agents overlooking common-sense constraints. Consequently, Constrained Reinforcement Learning (CRL) is a natural choice for safe decisions. However, specifying the exact cost function is inherently difficult in healthcare. Recent Inverse Constrained Reinforcement Learning (ICRL) is a promising approach that infers constraints from expert demonstrations. ICRL algorithms model Markovian decisions in an interactive environment. These settings do not align with the practical requirement of a decision-making system in healthcare, where decisions rely on historical treatment recorded in an offline dataset. To tackle these issues, we propose the Constraint Transformer (CT). Specifically, 1) we utilize a causal attention mechanism to incorporate historical decisions and observations into the constraint modeling, while employing a Non-Markovian layer for weighted constraints to capture critical states. 2) A generative world model is used to perform exploratory data augmentation, enabling offline RL methods to simulate unsafe decision sequences. In multiple medical scenarios, empirical results demonstrate that CT can capture unsafe states and achieve strategies that approximate lower mortality rates, reducing the occurrence probability of unsafe behaviors.

Offline Inverse Constrained Reinforcement Learning for Safe-Critical Decision Making in Healthcare

TL;DR

The Constraint Transformer (CT) is proposed, which utilizes a causal attention mechanism to incorporate historical decisions and observations into the constraint modeling, while employing a Non-Markovian layer for weighted constraints to capture critical states.

Abstract

Reinforcement Learning (RL) applied in healthcare can lead to unsafe medical decisions and treatment, such as excessive dosages or abrupt changes, often due to agents overlooking common-sense constraints. Consequently, Constrained Reinforcement Learning (CRL) is a natural choice for safe decisions. However, specifying the exact cost function is inherently difficult in healthcare. Recent Inverse Constrained Reinforcement Learning (ICRL) is a promising approach that infers constraints from expert demonstrations. ICRL algorithms model Markovian decisions in an interactive environment. These settings do not align with the practical requirement of a decision-making system in healthcare, where decisions rely on historical treatment recorded in an offline dataset. To tackle these issues, we propose the Constraint Transformer (CT). Specifically, 1) we utilize a causal attention mechanism to incorporate historical decisions and observations into the constraint modeling, while employing a Non-Markovian layer for weighted constraints to capture critical states. 2) A generative world model is used to perform exploratory data augmentation, enabling offline RL methods to simulate unsafe decision sequences. In multiple medical scenarios, empirical results demonstrate that CT can capture unsafe states and achieve strategies that approximate lower mortality rates, reducing the occurrence probability of unsafe behaviors.

Paper Structure

This paper contains 35 sections, 12 equations, 15 figures, 11 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Works
  3. Problem Formulation
  4. Method
  5. Constraint Transformer
  6. Model-based Offline RL
  7. Safe-Critical Decision Making with Constraints
  8. Experiment
  9. Can Offline CT Learn Effective Constraints?
  10. Can Offline CT Improve the Performance of CRL?
  11. Can CRL with Offline CT Learn Safe Policies?
  12. Off-policy evaluation
  13. Conclusion
  14. Problem Define
  15. Sepsis Problem Define
  16. ...and 20 more sections

Figures (15)

  • Figure 1: The distribution of vaso for patients with the same state. Physicians make different decisions by referencing historical information, whereas agents, based on the Markov assumption, can only make the same decision.
  • Figure 2: The overview of the safe healthcare policy learning with offline CT.
  • Figure 3: The structure of the CT.
  • Figure 4: The structure of the model-based offline RL.
  • Figure 5: The relationship between cost and mortality.
  • ...and 10 more figures