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001433106 001__ 1433106
001433106 003__ OCoLC
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001433106 019__ $$a1232282410$$a1235826175$$a1238202428$$a1238205434$$a1238205898$$a1240162866$$a1240530996
001433106 020__ $$a9781484265031$$q(electronic bk.)
001433106 020__ $$a1484265033$$q(electronic bk.)
001433106 020__ $$z9781484265024
001433106 020__ $$z1484265025
001433106 020__ $$z9781484265048$$q(print)
001433106 020__ $$z1484265041
001433106 0247_ $$a10.1007/978-1-4842-6503-1$$2doi
001433106 0248_ $$a9781484265024
001433106 0248_ $$a9781484265031
001433106 035__ $$aSP(OCoLC)1228457044
001433106 040__ $$aYDX$$beng$$erda$$epn$$cYDX$$dEBLCP$$dVT2$$dDCT$$dGW5XE$$dERF$$dLDP$$dTOH$$dOCLCO$$dOCLCF$$dOCL$$dOCLCQ$$dOCLCO$$dCOM$$dN$T$$dOCLCQ
001433106 049__ $$aISEA
001433106 050_4 $$aQA76.76.C672
001433106 08204 $$a794.8/151$$223
001433106 1001_ $$aMajumder, Abhilash,$$eauthor.
001433106 24510 $$aDeep reinforcement learning in Unity :$$bwith Unity ML toolkit /$$cAbhilash Majumder.
001433106 264_1 $$a[Berkeley] :$$bApress,$$c[2021]
001433106 300__ $$a1 online resource
001433106 336__ $$atext$$btxt$$2rdacontent
001433106 337__ $$acomputer$$bc$$2rdamedia
001433106 338__ $$aonline resource$$bcr$$2rdacarrier
001433106 347__ $$atext file
001433106 500__ $$aIncludes index.
001433106 5050_ $$aChapter 1: Introduction to Reinforcement Learning -- Chapter 2: Path Finding and Navigation -- Chapter 3: Setting Up ML Agents Toolkit -- Chapter 4: Understanding Brain Agents and Academy -- Chapter 5: Deep Reinforcement Learning -- Chapter 6: Competitive Networks for AI Agents -- Chapter 7: Case Studies in ML Agents.
001433106 506__ $$aAccess limited to authorized users.
001433106 520__ $$aGain an in-depth overview of reinforcement learning for autonomous agents in game development with Unity. This book starts with an introduction to state-based reinforcement learning algorithms involving Markov models, Bellman equations, and writing custom C# code with the aim of contrasting value and policy-based functions in reinforcement learning. Then, you will move on to path finding and navigation meshes in Unity, setting up the ML Agents Toolkit (including how to install and set up ML agents from the GitHub repository), and installing fundamental machine learning libraries and frameworks (such as Tensorflow). You will learn about: deep learning and work through an introduction to Tensorflow for writing neural networks (including perceptron, convolution, and LSTM networks), Q learning with Unity ML agents, and porting trained neural network models in Unity through the Python-C# API. You will also explore the OpenAI Gym Environment used throughout the book. Deep Reinforcement Learning in Unity provides a walk-through of the core fundamentals of deep reinforcement learning algorithms, especially variants of the value estimation, advantage, and policy gradient algorithms (including the differences between on and off policy algorithms in reinforcement learning). These core algorithms include actor critic, proximal policy, and deep deterministic policy gradients and its variants. And you will be able to write custom neural networks using the Tensorflow and Keras frameworks. Deep learning in games makes the agents learn how they can perform better and collect their rewards in adverse environments without user interference. The book provides a thorough overview of integrating ML Agents with Unity for deep reinforcement learning. You will: Understand how deep reinforcement learning works in games Grasp the fundamentals of deep reinforcement learning Integrate these fundamentals with the Unity ML Toolkit SDK Gain insights into practical neural networks for training Agent Brain in the context of Unity ML Agents Create different models and perform hyper-parameter tuning Understand the Brain-Academy architecture in Unity ML Agents Understand the Python-C# API interface during real-time training of neural networks Grasp the fundamentals of generic neural networks and their variants using Tensorflow Create simulations and visualize agents playing games in Unity.
001433106 542__ $$fCopyright © Apress$$g2021
001433106 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed March 3, 2021).
001433106 63000 $$aUnity (Electronic resource)
001433106 650_0 $$aComputer games$$xProgramming.
001433106 650_0 $$aIntelligent agents (Computer software)
001433106 650_0 $$aArtificial intelligence.
001433106 650_6 $$aJeux d'ordinateur$$xProgrammation.
001433106 650_6 $$aAgents intelligents (Logiciels)
001433106 650_6 $$aIntelligence artificielle.
001433106 655_0 $$aElectronic books.
001433106 77608 $$iPrint version:$$aMajumder, Abhilash.$$tDeep reinforcement learning in Unity.$$d[Berkeley] : Apress, [2021]$$z1484265025$$z9781484265024$$w(OCoLC)1193111503
001433106 852__ $$bebk
001433106 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-1-4842-6503-1$$zOnline Access$$91397441.1
001433106 909CO $$ooai:library.usi.edu:1433106$$pGLOBAL_SET
001433106 980__ $$aBIB
001433106 980__ $$aEBOOK
001433106 982__ $$aEbook
001433106 983__ $$aOnline
001433106 994__ $$a92$$bISE