Acceleration in Policy Optimization

We work towards a unifying paradigm for accelerating policy optimization methods in reinforcement learning (RL) by integrating foresight in the policy improvement step via optimistic and adaptive updates. Leveraging the connection between policy iteration and policy gradient methods, we view policy optimization algorithms as iteratively solving a sequence of surrogate objectives, local lower bounds on the original objective. We define optimism as predictive modelling of the future behavior of a policy, and adaptivity as taking immediate and anticipatory corrective actions to mitigate accumulating errors from overshooting predictions or delayed responses to change. We use this shared lens to jointly express other well-known algorithms, including model-based policy improvement based on forward search, and optimistic meta-learning algorithms. We analyze properties of this formulation, and show connections to other accelerated optimization algorithms. Then, we design an optimistic policy gradient algorithm, adaptive via meta-gradient learning, and empirically highlight several design choices pertaining to acceleration, in an illustrative task

Prevalence of Gestational Diabetes in preCOVID-19 and COVID-19 Years and Its Impact on Pregnancy: A 5-Year Retrospective Study

Gestational diabetes mellitus (GDM) affects a total of 3% to 9% of all pregnancies. It has a high impact on both mother and baby, increases the perinatal risks, and predicts the presence of long-term chronic metabolic complications. The aim of our study is to determine the incidence of GDM in tertiary hospitals in the west part of Romania to lay out the risk factors associated with GDM and to observe the evolution of pregnancy among patients with this pathology by emphasizing the state of birth of the fetus, the birth weight, and the way of birth. We also want to compare the prevalence of GDM in preCOVID-19 (Coronavirus disease) versus COVID-19 years. The study took place between January 2017 and December 2021 at the Municipal Emergency Hospital of Timisoara, Romania. The proportion of births with GDM was significantly increased during the COVID-19 period compared to the preCOVID-19 period (chi2 Fisher exact test, p < 0.001). The period 2020–2021 represents a significant risk factor for GDM births (OR = 1.87, with 95% CI = [1.30, 2.67]). COVID years represent a risk period for developing gestational diabetes, which can be explained by reduced physical activity, anxiety, or modified dietary habits, even if the follow-up period was not impacted