Quantum reinforcement learning during human decision-making

Li, Jian; Dong, Daoyi; Wei, Zhengde; Liu, Ying; Pan, Yu; Nori, Franco; Zhang, Xiaochu

doi:10.1038/s41562-019-0804-2

Cited by 98 publications

(51 citation statements)

References 93 publications

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“…We, however, directly modeled human decision as quantum states and decision-selection as quantum interaction, bypassing the utility-based quantum theoretic approach. Our approach is similar to the value-based quantum reinforcement learning (QRL) framework by Li et al 23 – 25 . Li et al in their study 25 demonstrated a QRL framework to be a better alternative for mimicking human decision-making, in which 2 QRL and 12 classical RL models were compared in modeling human decision-making among healthy and cigarette-smoking subjects while performing the Iowa Gambling Task.…”

Section: Introductionmentioning

confidence: 99%

Quantum deep reinforcement learning for clinical decision support in oncology: application to adaptive radiotherapy

Niraula

Jamaluddin

Matuszak

et al. 2021

Sci Rep

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Subtle differences in a patient’s genetics and physiology may alter radiotherapy (RT) treatment responses, motivating the need for a more personalized treatment plan. Accordingly, we have developed a novel quantum deep reinforcement learning (qDRL) framework for clinical decision support that can estimate an individual patient’s dose response mid-treatment and recommend an optimal dose adjustment. Our framework considers patients’ specific information including biological, physical, genetic, clinical, and dosimetric factors. Recognizing that physicians must make decisions amidst uncertainty in RT treatment outcomes, we employed indeterministic quantum states to represent human decision making in a real-life scenario. We paired quantum decision states with a model-based deep q-learning algorithm to optimize the clinical decision-making process in RT. We trained our proposed qDRL framework on an institutional dataset of 67 stage III non-small cell lung cancer (NSCLC) patients treated on prospective adaptive protocols and independently validated our framework in an external multi-institutional dataset of 174 NSCLC patients. For a comprehensive evaluation, we compared three frameworks: DRL, qDRL trained in a Qiskit quantum computing simulator, and qDRL trained in an IBM quantum computer. Two metrics were considered to evaluate our framework: (1) similarity score, defined as the root mean square error between retrospective clinical decisions and the AI recommendations, and (2) self-evaluation scheme that compares retrospective clinical decisions and AI recommendations based on the improvement in the observed clinical outcomes. Our analysis shows that our framework, which takes into consideration individual patient dose response in its decision-making, can potentially improve clinical RT decision-making by at least about 10% compared to unaided clinical practice. Further validation of our novel quantitative approach in a prospective study will provide a necessary framework for improving the standard of care in personalized RT.

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Section: Introductionmentioning

confidence: 99%

Quantum deep reinforcement learning for clinical decision support in oncology: application to adaptive radiotherapy

Niraula

Jamaluddin

Matuszak

et al. 2021

Sci Rep

View full text Add to dashboard Cite

show abstract

“…The predefined DCC area was mainly in the ACC in this study. The ACC has been related to the function of cognitive control of emotion (Bush et al, 2000;Disner et al, 2011;Li et al, 2020;Ochsner and Gross, 2005). Individuals with a greater activation in the ACC when viewing sad faces may have a deficient inhibition ability and thus require greater cognitive effort to divert attention away from negative stimuli (Disner et al, 2011).…”

Section: Ll Open Accessmentioning

confidence: 99%

Outbreak of COVID-19 altered the relationship between memory bias and depressive degree in nonclinical depression

Zhang¹,

Piao²,

Chen³

et al. 2021

iScience

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The outbreak of the novel coronavirus disease 2019 (COVID-19) has increased concern about people's mental health under such serious stressful situation, especially depressive symptoms. Cognitive biases have been related to depression degree in previous studies. Here, we used behavioral and brain imaging analysis, to determine if and how the COVID-19 pandemic affects the relationship between current cognitive biases and future depression degree and the underlying neural basis in a nonclinical depressed population. An out-expectation result showed that a more negative memory bias was associated with a greater decrease in future depressive indices in nonclinical depressed participants during the COVID-19 pandemic, which might be due to decreased social stress. These data enhance our understanding of how the depressive degree of nonclinical depressed populations will change during the COVID-19 pandemic and also provide support for social distancing policies from a psychological perspective.

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“…Therefore, an adversarial cost sequence associated with each node is assumed, and a widely used variation constraint is introduced on each cost sequence. To cope with the objective of sequential decision strategy, the problem is formulated using the reinforcement learning framework (Li et al, 2020;Littman, 2015;Sutton and Barto, 2018). Specifically, the Bayesian prior method (Smith and Paté-Cornell, 2018) is employed for the model parameters and the problem is formulated as a Bayesian adversarial multi-node bandit (MNB) model.…”

Section: Introductionmentioning

confidence: 99%

Bayesian adversarial multi-node bandit for optimal smart grid protection against cyber attacks

Liu

et al. 2021

Automatica

Self Cite

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The cybersecurity of smart grids has become one of key problems in developing reliable modern power and energy systems. This paper introduces a non-stationary adversarial cost with a variation constraint for smart grids and enables us to investigate the problem of optimal smart grid protection against cyber attacks in a relatively practical scenario. In particular, a Bayesian multi-node bandit (MNB) model with adversarial costs is constructed and a new regret function is defined for this model. An algorithm called Thompson-Hedge algorithm is presented to solve the problem and the superior performance of the proposed algorithm is proven in terms of the convergence rate of the regret function. The applicability of the algorithm to real smart grid scenarios is verified and the performance of the algorithm is also demonstrated by numerical examples.

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Quantum reinforcement learning during human decision-making

Cited by 98 publications

References 93 publications

Quantum deep reinforcement learning for clinical decision support in oncology: application to adaptive radiotherapy

Quantum deep reinforcement learning for clinical decision support in oncology: application to adaptive radiotherapy

Outbreak of COVID-19 altered the relationship between memory bias and depressive degree in nonclinical depression

Bayesian adversarial multi-node bandit for optimal smart grid protection against cyber attacks

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