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POPCORN: Partially Observed Prediction Constrained Reinforcement Learning AUTHORS: JOSEPH FUTOMA, MICHAEL C. HUGHES, FINALE DOSHI-VELEZ presenter: Zhongwen Zhang CS885 University of Waterloo July, 2020 Overview Problem:

  1. POPCORN: Partially Observed Prediction Constrained Reinforcement Learning AUTHORS: JOSEPH FUTOMA, MICHAEL C. HUGHES, FINALE DOSHI-VELEZ presenter: Zhongwen Zhang CS885 – University of Waterloo – July, 2020

  2. Overview ◆ Problem: decision-making for managing patients in ICU (Intensive Care Unit) with acute hypotension ◆ Challenges: Solutions: ◦ Medical environment is partially observable POMDP ◦ Model misspecification POPCORN ◦ Data limited OPE (Off Policy Evaluation) ◦ Data missing Generative model ◆ Importance: more effective treatment is badly needed

  3. Related work ◆ Model-free RL methods assuming full-observability [Komorowski et al., 2018] [Raghu et al., 2017] [Prasad et al., 2017] [Ernstet al., 2006] [Martín-Guerrero et al., 2009]. ◆ POMDP RL methods (two-stage fashion) [Hauskrecht and Fraser, 2000] [Li et al., 2018] [Oberst and Sontag, 2019] ◆ Decision-aware optimization: ◆ Mode-free [Karkus et al., 2017] 1. On-policy setting ◆ Model-based [Igl et al., 2018] 2. Features extracted from network

  4. High-level Idea ◆ Find a balance between purely maximum likelihood estimation (generative model) and purely reward-driven (discriminative model) extreme.

  5. Prediction-Constrained POMDPs ◆ Objective: ◆ Equivalently transformed objective: ◆ Optimization method: gradient descent

  6. Log Marginal Likelihood ℒ 𝑕𝑓𝑜 ◆ Computation: EM algorithm for HMM [Rabiner, 1989] ◆ Parameter set: Estimated separately

  7. Computing the value term 𝑊(𝜌 𝜄 ) ◆ Step1: Computing 𝜌 𝜄 by PBVI (Point-Based Value Iteration) ◆ Step2: Computing 𝑊(𝜌 𝜄 ) by OPE

  8. Computing the value term 𝑊(𝜌 𝜄 ) ◆ Step1: Computing 𝜌 𝜄 by PBVI (Point-Based Value Iteration) [Joelle Pineau, et.al., 2003] ◆ Exact value iteration costs exponential time complexity ◆ Approximation by only computing the value for a set of belief points polynomial time complexity 𝑊 = {𝛽 0 , 𝛽 1 , 𝛽 2 , 𝛽 3 } 𝑐 0 𝑐 2 𝑐 3 𝑐 1

  9. Computing the value term 𝑊(𝜌 𝜄 ) ◆ Step1: Computing 𝜌 𝜄 by PBVI (Point-Based Value Iteration) ◆ Step2: Computing 𝑊(𝜌 𝜄 ) by OPE ◆ 𝜌 𝜄 vs. 𝜌 𝑐𝑓ℎ𝑏𝑤𝑗𝑝𝑠 ◆ Importance sampling: ◆ Lower bias under some mild assumption ◆ Sample efficient

  10. Empirical evaluation ◆ Simulated environments ◆ Synthetic domain ◆ Sepsis simulator ◆ Real data application: hypotension

  11. Synthetic domain problem setting: ? ?

  12. Synthetic domain finding relevant advantage of robust to signal dimension generative model misspecified model

  13. Sepsis Simulator ◆ Medically-motivated environment with known ground truth ◆ Results:

  14. Real Data Application: Hypotension

  15. Real Data Application: Hypotension MAP: mean arterial pressure

  16. Future directions ◆ Scaling to environments with more complex state structures ◆ Long-term temporal dependencies ◆ Investigating semi-supervised settings where not all sequences have rewards ◆ Ultimately become integrated into clinical decision support tools

  17. References  Komorowski, M., Celi, L.A., Badawi, O. et al. The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care. Nat Med 24, 1716 – 1720 (2018). https://doi.org/10.1038/s41591-018-0213-5  Raghu, Aniruddh, et al. "Continuous state-space models for optimal sepsis treatment-a deep reinforcement learning approach." arXiv preprint arXiv:1705.08422 (2017).  Prasad, Niranjani, et al. "A reinforcement learning approach to weaning of mechanical ventilation in intensive care units." arXiv preprint arXiv:1704.06300 (2017).  Ernst, Damien, et al. "Clinical data based optimal STI strategies for HIV: a reinforcement learning approach." Proceedings of the 45th IEEE Conference on Decision and Control . IEEE, 2006.  Martín-Guerrero, José D., et al. "A reinforcement learning approach for individualizing erythropoietin dosages in hemodialysis patients." Expert Systems with Applications 36.6 (2009): 9737-9742.  Hauskrecht, Milos, and Hamish Fraser. "Planning treatment of ischemic heart disease with partially observable Markov decision processes." Artificial Intelligence in Medicine 18.3 (2000): 221-244.  Li, Luchen, Matthieu Komorowski, and Aldo A. Faisal. "The actor search tree critic (ASTC) for off-policy POMDP learning in medical decision making." arXiv preprint arXiv:1805.11548 (2018).  Oberst, Michael, and David Sontag. "Counterfactual off-policy evaluation with gumbel-max structural causal models." arXiv preprint arXiv:1905.05824 (2019).  Karkus, Peter, David Hsu, and Wee Sun Lee. "Qmdp-net: Deep learning for planning under partial observability." Advances in Neural Information Processing Systems . 2017.  Igl, Maximilian, et al. "Deep variational reinforcement learning for POMDPs." arXiv preprint arXiv:1806.02426 (2018).  Pineau, Joelle, Geoff Gordon, and Sebastian Thrun. "Point-based value iteration: An anytime algorithm for POMDPs." IJCAI . Vol. 3. 2003.  Rabiner, Lawrence R. "A tutorial on hidden Markov models and selected applications in speech recognition." Proceedings of the IEEE 77.2 (1989): 257-286.

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