Gridworld Answers
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Grid World
It is the year 2091 and Grid World is now a paradise world under serious threat. Marc Wilson was one of the first people to be born in this digital paradise and he has now lived there for thirty years. With the help of those that had followed his father Paul and those since born here Grid World was grown into a beautiful paradise world with no crime, disease or famine. Everyone does their part for the good of Grid World and everyone is treated on equal terms no matter their gender, colour or sexual preference. Sadly life is still very different back in what is termed ‘the real world’. Continual global wars, devastation and starvation has continued to ravage the population and those few that still survive are now nothing more than slaves working for the two surviving Warlords and their murderous armies. Both warlords are very aware of the existence of Grid World and when one of them has gained total global dominance they will begin to cast their murderous attention towards the digital utopia that exists below his feet. In fact it is very possible an attack of some form has already begun, Grid World is showing the first signs of instability Can Grid World survive against an army that has over one million battle hardened soldiers and an insane, genocidal leader? Will they fall the way of the nations on the planet’s surface or will Marc lead them to victory. Who, if any, will survive?
Java Deep Learning Projects
Author: Md. Rezaul Karim
language: en
Publisher: Packt Publishing Ltd
Release Date: 2018-06-29
Build and deploy powerful neural network models using the latest Java deep learning libraries Key Features Understand DL with Java by implementing real-world projects Master implementations of various ANN models and build your own DL systems Develop applications using NLP, image classification, RL, and GPU processing Book Description Java is one of the most widely used programming languages. With the rise of deep learning, it has become a popular choice of tool among data scientists and machine learning experts. Java Deep Learning Projects starts with an overview of deep learning concepts and then delves into advanced projects. You will see how to build several projects using different deep neural network architectures such as multilayer perceptrons, Deep Belief Networks, CNN, LSTM, and Factorization Machines. You will get acquainted with popular deep and machine learning libraries for Java such as Deeplearning4j, Spark ML, and RankSys and you’ll be able to use their features to build and deploy projects on distributed computing environments. You will then explore advanced domains such as transfer learning and deep reinforcement learning using the Java ecosystem, covering various real-world domains such as healthcare, NLP, image classification, and multimedia analytics with an easy-to-follow approach. Expert reviews and tips will follow every project to give you insights and hacks. By the end of this book, you will have stepped up your expertise when it comes to deep learning in Java, taking it beyond theory and be able to build your own advanced deep learning systems. What you will learn Master deep learning and neural network architectures Build real-life applications covering image classification, object detection, online trading, transfer learning, and multimedia analytics using DL4J and open-source APIs Train ML agents to learn from data using deep reinforcement learning Use factorization machines for advanced movie recommendations Train DL models on distributed GPUs for faster deep learning with Spark and DL4J Ease your learning experience through 69 FAQs Who this book is for If you are a data scientist, machine learning professional, or deep learning practitioner keen to expand your knowledge by delving into the practical aspects of deep learning with Java, then this book is what you need! Get ready to build advanced deep learning models to carry out complex numerical computations. Some basic understanding of machine learning concepts and a working knowledge of Java are required.
Explainable and Interpretable Reinforcement Learning for Robotics
This book surveys the state of the art in explainable and interpretable reinforcement learning (RL) as relevant for robotics. While RL in general has grown in popularity and been applied to increasingly complex problems, several challenges have impeded the real-world adoption of RL algorithms for robotics and related areas. These include difficulties in preventing safety constraints from being violated and the issues faced by systems operators who desire explainable policies and actions. Robotics applications present a unique set of considerations and result in a number of opportunities related to their physical, real-world sensory input and interactions. The authors consider classification techniques used in past surveys and papers and attempt to unify terminology across the field. The book provides an in-depth exploration of 12 attributes that can be used to classify explainable/interpretable techniques. These include whether the RL method is model-agnostic or model-specific, self-explainable or post-hoc, as well as additional analysis of the attributes of scope, when-produced, format, knowledge limits, explanation accuracy, audience, predictability, legibility, readability, and reactivity. The book is organized around a discussion of these methods broken down into 42 categories and subcategories, where each category can be classified according to some of the attributes. The authors close by identifying gaps in the current research and highlighting areas for future investigation.