03845nam 2200481 450 991082581800332120230530205623.01-78913-186-3(CKB)4100000005116231(Au-PeEL)EBL5446051(CaPaEBR)ebr11590663(OCoLC)1045010181(CaSebORM)9781789138139(MiAaPQ)EBC5446051(PPN)233397248(EXLCZ)99410000000511623120180808d2018 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierLearn Unity ML-Agents fundamentals of Unity machine learning : incorporate new powerful ML algorithms such as deep reinforcement learning for games /Michael Lanham1st editionBirmingham ;Mumbai :Packt,2018.1 online resource (197 pages) illustrations1-78913-813-2 Transform games into environments using machine learning and Deep learning with Tensorflow, Keras, and Unity About This Book Learn how to apply core machine learning concepts to your games with Unity Learn the Fundamentals of Reinforcement Learning and Q-Learning and apply them to your games Learn How to build multiple asynchronous agents and run them in a training scenario Who This Book Is For This book is intended for developers with an interest in using Machine learning algorithms to develop better games and simulations with Unity. The reader will be required to have a working knowledge of C# and a basic understanding of Python. What You Will Learn Develop Reinforcement and Deep Reinforcement Learning for games. Understand complex and advanced concepts of reinforcement learning and neural networks Explore various training strategies for cooperative and competitive agent development Adapt the basic script components of Academy, Agent, and Brain to be used with Q Learning. Enhance the Q Learning model with improved training strategies such as Greedy-Epsilon exploration Implement a simple NN with Keras and use it as an external brain in Unity Understand how to add LTSM blocks to an existing DQN Build multiple asynchronous agents and run them in a training scenario In Detail Unity Machine Learning agents allow researchers and developers to create games and simulations using the Unity Editor, which serves as an environment where intelligent agents can be trained with machine learning methods through a simple-to-use Python API. This book takes you from the basics of Reinforcement and Q Learning to building Deep Recurrent Q-Network agents that cooperate or compete in a multi-agent ecosystem. You will start with the basics of Reinforcement Learning and how to apply it to problems. Then you will learn how to build self-learning advanced neural networks with Python and Keras/TensorFlow. From there you move o n to more advanced training scenarios where you will learn further innovative ways to train your network with A3C, imitation, and curriculum learning models. By the end of the book, you will have learned how to build more complex environments by building a cooperative and competitive multi-agent ecosystem. Style and approach This book focuses on the foundations of ML, RL and DL for building agents in a game or simulationVideo gamesProgrammingMachine learningApplication softwareDevelopmentVideo gamesProgramming.Machine learning.Application softwareDevelopment.794.81526Lanham Micheal883448MiAaPQMiAaPQMiAaPQBOOK9910825818003321Learn Unity ML-Agents4104994UNINA03507nam 22008295 450 991096071420332120240307121453.097866113636049781281363602128136360X9781403977236140397723210.1057/9781403977236(CKB)1000000000342693(SSID)ssj0000096851(PQKBManifestationID)11108981(PQKBTitleCode)TC0000096851(PQKBWorkID)10113097(PQKB)10344559(DE-He213)978-1-4039-7723-6(MiAaPQ)EBC307561(Au-PeEL)EBL307561(CaPaEBR)ebr10135724(CaONFJC)MIL136360(OCoLC)560470799(Perlego)3496244(EXLCZ)99100000000034269320151222d2006 u| 0engurnn#008mamaatxtccrAction Figures Men, Action Films, and Contemporary Adventure Narratives /by M. Gallagher1st ed. 2006.New York :Palgrave Macmillan US :Imprint: Palgrave Macmillan,2006.1 online resource (240 p.)Bibliographic Level Mode of Issuance: Monograph9781349531691 1349531693 9781403970121 1403970122 Includes bibliographical references (p. [223]-226) and index.Introduction : popular representations of active masculinity since the late 1960s -- 1. Armchair thrills and the new adventurer -- 2. "I married Rambo" : action, spectacle, and melodrama -- 3. Omega men : late 1960s and early 1970s action heroes -- 4. Airport fiction : the men of mass-market literature -- 5. Restaging heroic masculinity : Jackie Chan and the Hong Kong action film -- Conclusion : the future of active masculinity.What accounts for the massive global popularity of action films and adventure literature? How do men and women respond to iconic screen stars such as Jackie Chan, Arnold Schwarzenegger, Steve McQueen, and Charlton Heston? Action genres have been Hollywood's most profitable global exports for most of its history, their male heroes the subject of much fascination and derision. Bestselling literary thrillers, from The Hunt for Red October to Into Thin Air , have also contributed markedly to popular understandings of male activity. Action Figures takes stock of action narratives' many appeals and recognizes how contemporary crises of gender identity manifest themselves in popular commercial texts.SexCultureMotion picturesTelevision broadcastingEthnologyGender StudiesSociology of CultureFilm and Television StudiesRegional Cultural StudiesSex.Culture.Motion pictures.Television broadcasting.Ethnology.Gender Studies.Sociology of Culture.Film and Television Studies.Regional Cultural Studies.791.43/655Gallagher Mark1968-1791120MiAaPQMiAaPQMiAaPQBOOK9910960714203321Action Figures4328016UNINA