ISBN-13: 9781484265024 / Angielski / Miękka / 2020 / 564 str.
ISBN-13: 9781484265024 / Angielski / Miękka / 2020 / 564 str.
Intermediate-Advanced user level
Chapter 1: Introduction to Reinforcement Learning
Sub -Topics
1. Markov Models and State Based Learning
2. Bellman Equations
3. Creating a Multi Armed Bandit RL simulation.
4. Value and Policy iteration.
Chapter 2: Pathfinding and Navigation
Sub - Topics
1. Pathfinding in Unity
2. Navigation Meshes
3. Creating Enemy AI
Chapter 3: Setting Up ML Agents Toolkit SDK
Sub - Topics:
1. Installing ML Agents
2. Configuring Brain Academy
3. Linking ML Agents with Tensorflow with Jupyter Notebooks
4. Playing with ML agents samples
Chapter 4: Understanding Brain Agents and Academy
Sub - Topics:
1. Understanding the architecture of Brain
2. Training different Agents with Single Brain
3. Generic Hyperparameters
Chapter 5: Deep Reinforcement Learning
Sub - Topics:
1. Fundamentals of Mathematical Machine Learning with Python
2. Deep Learning with Keras and Tensorflow
3. Deep Reinforcement Learning Algorithms
4. Writing neural network for Deep Q learning for Brain
5. Hyperparameter Tuning for Optimization
6. Memory-based LSTM Network Design with Keras for Brain
7. Building an AI Agent for Kart Game Using Trained NetworkChapter 6: Competitive Networks for AI Agents
Sub - Topics:
1. Cooperative Network and Adversarial Network
2. Extended Reinforcement Learning–Deep Policy Gradients3. Simulations Made with Unity ML Agents
4. Simulating AI Autonomous Agent for Self-driving
Chapter 7: Case Study – Obstacle Tower Challenge
Sub - Topics:
1. Obstacle Tower Challenge
2. Unity ML Agents Challenge
3. Research Developments from Unity AI
4. Playing with the Open AI Gym Wrapper
Abhilash Majumder is a natural language processing research engineer for HSBC (UK/India) and technical mentor for Udactiy (ML). He also has been associated with Unity Technologies and was a speaker at Unite India-18, and has educated close to 1,000 students from EMEA and SEPAC (India) on Unity. He is an ML contributor and curator for Open Source Google Research and Tensorflow, and creator of ML libraries under Python Package Index (Pypi). He is an online educationalist for Udemy and a deep learning mentor for Upgrad.
Abhilash was an apprentice/student ambassador for Unity Technologies where he educated corporate employees and students on using general Unity for game development. He was a technical mentor (AI programming) for the Unity Ambassadors Community and Content Production. He has been associated with Unity Technologies for general education, with an emphasis on graphics and machine learning. He is one of the first content creators for Unity Technologies India since 2017.
Gain 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:
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