AI for Classic Video Games using Reinforcement Learning

Deep reinforcement learning is a technique to teach machines tasks based on trial and error experiences in the way humans learn. In this paper, some preliminary research is done to understand how reinforcement learning and deep learning techniques can be combined to train an agent to play Archon, a classic video game. We compare two methods to estimate a Q function, the function used to compute the best action to take at each point in the game. In the first approach, we used a Q table to store the states and weights of the corresponding actions. In our experiments, this method converged very slowly. Our second approach was similar to that of [1]: We used a convolutional neural network (CNN) to determine a Q function. This deep neural network model successfully learnt to control the Archon player using keyboard event that it generated. We observed that the second approaches Q function converged faster than the first. For the latter method, the neural net was trained only using prediodic screenshots taken while it was playing. Experiments were conducted on a machine that did not have a GPU, so our training was slower as compared to [1]

TV Viewing versus Play - Trends and Impact on Obesity

A cross sectional descriptive study was conducted involving 10000 students from randomly selected government and private schools of Amritsar district to identify the trends of TV viewing and playing in school children aged 5 years and above, to compare TV viewing in both sexes, different age groups and urban versus rural children and to study the impact of TV viewing and playing on obesity in children. The results showed that TV viewing has replaced outdoor playing in most children, irrespective of age, sex and residence. A positive relation between TV viewing and obesity was also documented

Factors Contributing to Psycho-Social Ill-Health in Male Adolescents

Objective: To study the prevalence of psychosocial problems in male adolescents and find out various factors contributing to psycho-social ill health. Methods: 500 adolescents were interviewed using a pre-tested structured questionnaire to elicit the information about the psychosocial problems including depression, suicidal thoughts and suicidal attempts. Association of academic performance, family problems, psychological problems and substance abuse was also included. Results: More than one third (39.6%) adolescents were having psychological problems. These problems were significantly higher in middle adolescence (14-16 years), large extended families (> 8 members) and lower socioeconomic status. Residence had no significant relation to psychological problems in the adolescents. On correlation, these adolescents with psychological problems were having significantly more academic problems, family disputes, domestic violence, lesser number of close friends and greater substance abuse. Conclusion: Considering that male adolescents from large families with lesser education and lower income had higher prevalence of psychosocial problems, it is essential for health care planners to design comprehensive family and health education programs for the adolescents. The family support, teacher student rapport and peer group communication should be strengthened to counteract unsafe behaviours in the adolescents

A Sustainability Disclosure Index Using Corporate Sustainability Reports

Background: There are already many indices such as Bloomberg’s environmental-and-social governance (ESG) ratings and the Dow Jones Sustainability Indices (DSJI), which use proprietary methods to rate companies using private and publicly available information processed with proprietary methods. This paper seeks to develop a formative index for researchers and practitioners using only publicly available sustainability reports with a transparent procedure. Methods: Thirty-two indicators, obtained in an earlier study from the literature, GRI, and other sources, were adopted. The sustainability report of each of 331 companies was then scored on a discrete 0–3 scale for each indicator as regards disclosure. The index for the company then is simply a summation of the indicator scores. Tests were conducted to see if the index can be (a) used for companies with different revenues and from different sectors and (b) tested for explaining DJSI or ESG ratings. Results: The index can be used for companies with a wide range of revenues and from different sectors. Despite its simplicity, the disclosure index significantly explains the DJSI and ESG. Conclusions: A disclosure index for companies has been developed here using only their publicly available sustainability reports, unlike existing indices like the DJSI that use public and private information and proprietary methods. Researchers and financial institutions can use this index or develop their own indices by refining the methodology presented here