ViZDoom Competitions: Playing Doom from Pixels

This paper presents the first two editions of Visual Doom AI Competition, held in 2016 and 2017. The challenge was to create bots that compete in a multi-player deathmatch in a first-person shooter (FPS) game, Doom. The bots had to make their decisions based solely on visual information, i.e., a raw screen buffer. To play well, the bots needed to understand their surroundings, navigate, explore, and handle the opponents at the same time. These aspects, together with the competitive multi-agent aspect of the game, make the competition a unique platform for evaluating the state of the art reinforcement learning algorithms. The paper discusses the rules, solutions, results, and statistics that give insight into the agents' behaviors. Best-performing agents are described in more detail. The results of the competition lead to the conclusion that, although reinforcement learning can produce capable Doom bots, they still are not yet able to successfully compete against humans in this game. The paper also revisits the ViZDoom environment, which is a flexible, easy to use, and efficient 3D platform for research for vision-based reinforcement learning, based on a well-recognized first-person perspective game Doom

General-purpose and special-purpose visual systems

The information that eyes supply supports a wide variety of functions, from the guidance systems that enable an animal to navigate successfully around the environment, to the detection and identification of predators, prey, and conspecifics. The eyes with which we are most familiar the single-chambered eyes of vertebrates and cephalopod molluscs, and the compound eyes of insects and higher crustaceans allow these animals to perform the full range of visual tasks. These eyes have evidently evolved in conjunction with brains that are capable of subjecting the raw visual information to many different kinds of analysis, depending on the nature of the task that the animal is engaged in. However, not all eyes evolved to provide such comprehensive information. For example, in bivalve molluscs we find eyes of very varied design (pinholes, concave mirrors, and apposition compound eyes) whose only function is to detect approaching predators and thereby allow the animal to protect itself by closing its shell. Thus, there are special-purpose eyes as well as eyes with multiple functions

A design strategy for autonomous systems

Some solutions to crucial issues regarding the competent performance of an autonomously operating robot are identified; namely, that of handling multiple and variable data sources containing overlapping information and maintaining coherent operation while responding adequately to changes in the environment. Support for the ideas developed for the construction of such behavior are extracted from speculations in the study of cognitive psychology, an understanding of the behavior of controlled mechanisms, and the development of behavior-based robots in a few robot research laboratories. The validity of these ideas is supported by some simple simulation experiments in the field of mobile robot navigation and guidance

Breaking the Barriers to Specialty Care: Practical Ideas to Improve Health Equity and Reduce Cost - Call to Action for a System-wide Focus on Equity

Tremendous health outcome inequities remain in the U.S. across race and ethnicity, gender and sexual orientation, socio-economic status, and geography—particularly for those with serious conditions such as lung or skin cancer, HIV/AIDS, or cardiovascular disease.These inequities are driven by a complex set of factors—including distance to a specialist, insurance coverage, provider bias, and a patient's housing and healthy food access. These inequities not only harm patients, resulting in avoidable illness and death, they also drive unnecessary health systems costs.This 5-part series highlights the urgent need to address these issues, providing resources such as case studies, data, and recommendations to help the health care sector make meaningful strides toward achieving equity in specialty care.Top TakeawaysThere are vast inequalities in access to and outcomes from specialty health care in the U.S. These inequalities are worst for minority patients, low-income patients, patients with limited English language proficiency, and patients in rural areas.A number of solutions have emerged to improve health outcomes for minority and medically underserved patients. These solutions fall into three main categories: increasing specialty care availability, ensuring high-quality care, and helping patients engage in care.As these inequities are also significant drivers of health costs, payers, health care provider organizations, and policy makers have a strong incentive to invest in solutions that will both improve outcomes and reduce unnecessary costs. These actors play a critical role in ensuring that equity is embedded into core care delivery at scale.Part 5: "Call to Action for a System-wide Focus on Equity"These solutions create value not only for patients, but also for health care providers and public and private payers.  Each of these actors have a role to play in scaling and sustaining the health equity solutions.

Scoping the future: a model for integrating learning environments

The Virtual Learning Environment (VLE) has become synonymous with online learning in HE.However, with the rise of Web 2.0 technologies, social networking tools and cloud computing thearchitecture of the current VLEs is increasingly anachronistic. This paper suggests an alternative tothe traditional VLE: one which allows for flexibility and adaptation to the needs of individual teachers,while remaining resilient and providing students with a seamless experience. We present a prototypeof our vision, combining our new development software and a number of existing tried and tested toolsinto a single flexible interface, and built on established pedagogical and technical standards

Learning Deployable Navigation Policies at Kilometer Scale from a Single Traversal

Model-free reinforcement learning has recently been shown to be effective at learning navigation policies from complex image input. However, these algorithms tend to require large amounts of interaction with the environment, which can be prohibitively costly to obtain on robots in the real world. We present an approach for efficiently learning goal-directed navigation policies on a mobile robot, from only a single coverage traversal of recorded data. The navigation agent learns an effective policy over a diverse action space in a large heterogeneous environment consisting of more than 2km of travel, through buildings and outdoor regions that collectively exhibit large variations in visual appearance, self-similarity, and connectivity. We compare pretrained visual encoders that enable precomputation of visual embeddings to achieve a throughput of tens of thousands of transitions per second at training time on a commodity desktop computer, allowing agents to learn from millions of trajectories of experience in a matter of hours. We propose multiple forms of computationally efficient stochastic augmentation to enable the learned policy to generalise beyond these precomputed embeddings, and demonstrate successful deployment of the learned policy on the real robot without fine tuning, despite environmental appearance differences at test time. The dataset and code required to reproduce these results and apply the technique to other datasets and robots is made publicly available at rl-navigation.github.io/deployable

Breaking the Barriers to Specialty Care: Practical Ideas to Improve Health Equity and Reduce Cost - Striving for Equity in Specialty Care Full Report

Tremendous health outcome inequities remain in the U.S. across race and ethnicity, gender and sexual orientation, socio-economic status, and geography—particularly for those with serious conditions such as lung or skin cancer, HIV/AIDS, or cardiovascular disease.These inequities are driven by a complex set of factors—including distance to a specialist, insurance coverage, provider bias, and a patient's housing and healthy food access. These inequities not only harm patients, resulting in avoidable illness and death, they also drive unnecessary health systems costs.This 5-part series highlights the urgent need to address these issues, providing resources such as case studies, data, and recommendations to help the health care sector make meaningful strides toward achieving equity in specialty care.Top TakeawaysThere are vast inequalities in access to and outcomes from specialty health care in the U.S. These inequalities are worst for minority patients, low-income patients, patients with limited English language proficiency, and patients in rural areas.A number of solutions have emerged to improve health outcomes for minority and medically underserved patients. These solutions fall into three main categories: increasing specialty care availability, ensuring high-quality care, and helping patients engage in care.As these inequities are also significant drivers of health costs, payers, health care provider organizations, and policy makers have a strong incentive to invest in solutions that will both improve outcomes and reduce unnecessary costs. These actors play a critical role in ensuring that equity is embedded into core care delivery at scale.

Managament in a New and Experimentally Organized Economy

The parallel development of management theory and practice over three phases of economic development is surveyed; (1) the pre-oil crisis experience 1969-1975, (2) the post oil crisis sobering up through most of the 1990s and (3) the emergence of new global production organizations , blurring the notion of the firm to be managed. The external market circumstances of each period dictate different structures of business operations ; (a) a steady state and predictable environment, (b) crisis, inflation and disorderly markets and (c) new technology supporting a globally distributed production organization. As a consequence structural learning between the periods has been of limited value and often outright misleading. The influence of management theory on management practice and its origin in the received economic equilibrium model are discussed, and an alternative management theory based on the theory of the Experimentally Organized Economy (EOE) presented. The increased rate of failure among large firms is related to the increasing complexity of business decisions in globally distributed production and the decreased reliability of learning . It is concluded that successful management practice develops through experimentation in markets and that the best management education has been a varied career in many lines of business and in several companies.Competence bloc theory; Experimentally Organized Economy (EOE); Management theory; WAD theory; Firm Dynamics; Learning