Deep Reinforcement Learning on a Budget: 3D Control and Reasoning Without a Supercomputer

An important goal of research in Deep Reinforcement Learning in mobile robotics is to train agents capable of solving complex tasks, which require a high level of scene understanding and reasoning from an egocentric perspective. When trained from simulations, optimal environments should satisfy a currently unobtainable combination of high-fidelity photographic observations, massive amounts of different environment configurations and fast simulation speeds. In this paper we argue that research on training agents capable of complex reasoning can be simplified by decoupling from the requirement of high fidelity photographic observations. We present a suite of tasks requiring complex reasoning and exploration in continuous, partially observable 3D environments. The objective is to provide challenging scenarios and a robust baseline agent architecture that can be trained on mid-range consumer hardware in under 24h. Our scenarios combine two key advantages: (i) they are based on a simple but highly efficient 3D environment (ViZDoom) which allows high speed simulation (12000fps); (ii) the scenarios provide the user with a range of difficulty settings, in order to identify the limitations of current state of the art algorithms and network architectures. We aim to increase accessibility to the field of Deep-RL by providing baselines for challenging scenarios where new ideas can be iterated on quickly. We argue that the community should be able to address challenging problems in reasoning of mobile agents without the need for a large compute infrastructure

The Rhetoric of Video Games

Part of the Volume on the Ecology of Games: Connecting Youth, Games, and Learning Bogost's chapter offers an introduction to rhetoric in games. First he looks at the way games and their rules embody cultural values, following the work of Brian Sutton-Smith and looking in particular at a few examples from international sports. Then he discusses the relationship between games and ideology, showing how game play can unpack and expose deeply engrained social, cultural, and political assumptions. Finally he discusses the ways videogames make arguments. Drawing on the history of rhetoric, Bogost introduces a notion he calls "procedural rhetoric," the art of persuasion through rule-based representations and interactions

MIDGARD: A Simulation Platform for Autonomous Navigation in Unstructured Environments

We present MIDGARD, an open-source simulation platform for autonomous robot navigation in outdoor unstructured environments. MIDGARD is designed to enable the training of autonomous agents (e.g., unmanned ground vehicles) in photorealistic 3D environments, and to support the generalization skills of learning-based agents through the variability in training scenarios. MIDGARD's main features include a configurable, extensible, and difficulty-driven procedural landscape generation pipeline, with fast and photorealistic scene rendering based on Unreal Engine. Additionally, MIDGARD has built-in support for OpenAI Gym, a programming interface for feature extension (e.g., integrating new types of sensors, customizing exposing internal simulation variables), and a variety of simulated agent sensors (e.g., RGB, depth and instance/semantic segmentation). We evaluate MIDGARD's capabilities as a benchmarking tool for robot navigation utilizing a set of state-of-the-art reinforcement learning algorithms. The results demonstrate MIDGARD's suitability as a simulation and training environment, as well as the effectiveness of our procedural generation approach in controlling scene difficulty, which directly reflects on accuracy metrics. MIDGARD build, source code and documentation are available at https://midgardsim.org/

Hybrid 3D Rendering of Large Map Data for Crisis Management

In this paper we investigate the use of games technologies for the research and the development of 3D representations of real environments captured from GIS information and open source map data. Challenges involved in this area concern the large data-sets to be dealt with. Some existing map data include errors and are not complete, which makes the generation of realistic and accurate 3D environments problematic. The domain of application of our work is crisis management which requires very accurate GIS or map information. We believe the use of creating a 3D virtual environment using real map data whilst correcting and completing the missing data, improves the quality and performance of crisis management decision support system to provide a more natural and intuitive interface for crisis managers. Consequently, we present a case study into issues related to combining multiple large datasets to create an accurate representation of a novel, multi-layered, hybrid real-world maps. The hybrid map generation combines LiDAR, Ordnance Survey, and OpenStreetMap data to generate 3D cities spanning 1 km2. Evaluation of initial visualised scenes is presented. Initial tests consist of a 1 km2 landscape map containing up to 16 million vertices’ and run at an optimal 51.66 frames per-second

Procedural modeling of cities with semantic information for crowd simulation

En aquesta tesi de màster es presenta un sistema per a la generació procedural de ciutats poblades. Avui en dia poblar entorns virtuals grans tendeix a ser una tasca que requereix molt d’esforç i temps, i típicament la feina d’artistes o programadors experts. Amb aquest sistema es vol proporcionar una eina que permeti als usuaris generar entorns poblats d’una manera més fàcil i ràpida, mitjançat l’ús de tècniques procedurals. Les contribucions principals inclouen: la generació d’una ciutat virtual augmentada semànticament utilitzant modelat procedural basat en gramàtiques de regles, la generació dels seus habitants virtuals utilitzant dades estadístiques reals, i la generació d’agendes per a cada individu utilitzant també un mètode procedural basat en regles, el qual combina la informació semàntica de la ciutat amb les característiques i necessitats dels agents autònoms. Aquestes agendes individuals són usades per a conduir la simulació dels habitants, i poden incloure regles com a tasques d’alt nivell, l’avaluació de les quals es realitza al moment de començar-les. Això permet simular accions que depenguin del context, i interaccions amb altres agents.En esta tesis de máster se presenta un sistema para la generación procedural de ciudades pobladas. Hoy en día poblar entornos virtuales grandes tiende a ser una tarea que requiere de mucho tiempo y esfuerzo, y típicamente el trabajo de artistas o programadores expertos. Con este sistema se pretende proporcionar una herramienta que permita a los usuarios generar entornos poblados de un modo más fácil y rápido, mediante el uso de técnicas procedurales. Las contribuciones principales incluyen: la generación de una ciudad virtual aumentada semánticamente utilizando modelado procedural basado en gramáticas de reglas, la generación de sus habitantes virtuales utilizando datos estadísticos reales, y la generación de agendas para cada individuo utilizando también un método procedural basado en reglas, el cual combina la información semántica de la ciudad con las características y necesidades de los agentes autónomos. Estas agendas individuales son usadas para conducir la simulación de los habitantes, y pueden incluir reglas como tareas de alto nivel, la evaluación de las cuales se realiza cuando empiezan. Esto permite simular acciones que dependan del contexto, e interacciones con otros agentes.In this master thesis a framework for procedural generation of populated cities is presented. Nowadays, the population of large virtual environments tends to be a time-consuming task, usually requiring the work of expert artists or programmers. With this system we aim at providing a tool that can allow users to generate populated environments in an easier and faster way, by relying on the usage of procedural techniques. Our main contributions include: a generation of semantically augmented virtual cities using procedural modelling based on rule grammars, a generation of a virtual population using real-world data, and a generation of agendas for each individual inhabitant by using a procedural rule-based approach, which combines the city semantics with the autonomous agents characteristics and needs. The individual agendas are then used to drive a crowd simulation in the environment, and may include high-level rule tasks whose evaluation is delayed until they get triggered. This feature allows us to simulate context-dependant actions and interactions with other agents

Customizing Experiences for Mobile Virtual Reality

A criação manual de conteúdo para um jogo é um processo demorado e trabalhoso que requer um conjunto de habilidades diversi cado (normalmente designers, artistas e programadores) e a gestão de diferentes recursos (hardware e software especializados). Dado que o orçamento, tempo e recursos são frequentemente muito limitados, os projetos poderiam bene ciar de uma solução que permitisse poupar e investir noutros aspectos do desenvolvimento. No contexto desta tese, abordamos este desa o sugerindo a criação de pacotes especí cos para a geração de conteúdo per sonalizável, focados em aplicações de Realidade Virtual (RV) móveis. Esta abordagem divide o problema numa solução com duas facetas: em primeiro lugar, a Geração Procedural de Conteúdo, alcançada através de métodos convencionais e pela utilização inovadora de Grandes Modelos de Lin guagem (normalmente conhecidos por Large Language Models). Em segundo lugar, a Co-Criação de Conteúdo, que enfatiza o desenvolvimento colaborativo de conteúdo. Adicionalmente, dado que este trabalho se foca na compatibilidade com RV móvel, as limitações de hardware associadas a capacetes de RV autónomos (standalone VR Headsets) e formas de as ultrapassar são também abordadas. O conteúdo será gerado utilizando métodos actuais em geração procedural e facilitando a co-criação de conteúdo pelo utilizador. A utilização de ambas estas abordagens resulta em ambi entes, objectivos e conteúdo geral mais re-jogáveis com muito menos desenho. Esta abordagem está actualmente a ser aplicada no desenvolvimento de duas aplicações de RV distintas. A primeira, AViR, destina-se a oferecer apoio psicológico a indivíduos após a perda de uma gravidez. A se gunda, EmotionalVRSystem, visa medir as variações nas respostas emocionais dos participantes induzidas por alterações no ambiente, utilizando tecnologia EEG para leituras precisas

Quantifiable isovist and graph-based measures for automatic evaluation of different area types in virtual terrain generation

© 2013 IEEE. This article describes a set of proposed measures for characterizing areas within a virtual terrain in terms of their attributes and their relationships with other areas for incorporating game designers\u27 intent in gameplay requirement-based terrain generation. Examples of such gameplay elements include vantage point, strongholds, chokepoints and hidden areas. Our measures are constructed on characteristics of an isovist, that is, the volume of visible space at a local area and the connectivity of areas within the terrain. The calculation of these measures is detailed, in particular we introduce two new ways to accurately and efficiently calculate the 3D isovist volume. Unlike previous research that has mainly focused on aesthetic-based terrain generation, the proposed measures address a gap in gameplay requirement-based terrain generation-the need for a flexible mechanism to automatically parameterise specified areas and their associated relationships, capturing semantic knowledge relating to high level user intent associated with specific gameplay elements within the virtual terrain. We demonstrate applications of using the measures in an evolutionary process to automatically generate terrains that include specific gameplay elements as defined by a game designer. This is significant as this shows that the measures can characterize different gameplay elements and allow gameplay elements consistent with the designers\u27 intents to be generated and positioned in a virtual terrain without the need to specify low-level details at a model or logic level, hence leading to higher productivity and lower cost