Success stories of applied machine learning can be traced back to the datasets and environments that were put forward as challenges for the community. The challenge that the community sets as a benchmark is usually the challenge that the community eventually solves. The ultimate challenge of reinforcement learning research is to train real agents to operate in the real environment, but until now there has not been a common real-world RL benchmark. In this work, we present a prototype real-world environment from OffWorld Gym -a collection of real-world environments for reinforcement learning in robotics with free public remote access. Close integration into existing ecosystem allows the community to start using OffWorld Gym without any prior experience in robotics and takes away the burden of managing a physical robotics system, abstracting it under a familiar API. We introduce a navigation task, where a robot has to reach a visual beacon on an uneven terrain using only the camera input and provide baseline results in both the real environment and the simulated replica. To start training, visit https://gym.offworld.ai.
University of the Incarnate WordLearning Outcome: Describe the relationship between food insecurity, perceived stress, sleep, screen time, and physical activity among college students during the COVID-19 pandemic.Background: College students with high stress levels are more likely to engage in unhealthy behaviors, which may negatively impact their health. During COVID-19, college student's lives were disrupted on multiple levels. The aim of this study was to investigate the relationship between food insecurity, perceived stress, food insecurity, sleep, screen time, and physical activity among college students during the COVID-19 pandemic.Methods: College students at a private university (n¼154) completed an online survey to assess food insecurity (6-item Short Form of the U.S. Household Food Security Survey), stress (Perceived Stress Scale-10), physical activity (International Physical Activity Questionnaire Short Form), screen-time, and sleep. Student's self-reported demographic information. Independent T-test, Pearson correlation, and multiple-linear regression were used to analyze the data.Results: Among the participating students, 33.1% had high stress level, 54.5% had a moderate stress level, and 45.5% had some level of food insecurity. Lower levels of physical activity (t(152)¼ 2.551, p < 0.05) and 6 hours of sleep or less (t(152)¼4.602, p<0.001) were significantly related to greater levels of perceived stress. There was a significant positive correlation found between food insecurity and stress (r¼0.330, p<0.001). The regression model explained approximately 25% of variance of perceived stress (p<0.001).
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