Tabla de Contenidos: “…-- Model based learning and model free learning -- Monte Carlo learning -- Temporal difference learning -- On-policy and off-policy learning -- Q-learning -- The exploration exploitation dilemma -- Q-learning for the mountain car problem in OpenAI gym -- Deep Q-networks -- Using a convolution neural network instead of a single layer neural network -- Use of experience replay -- Separate target network to compute the target Q-values -- Advancements in deep Q-networks and beyond -- Double DQN -- Dueling DQN -- Deep Q-network for mountain car problem in OpenAI gym -- Deep Q-network for Cartpole problem in OpenAI gym -- Deep Q-network for Atari Breakout in OpenAI gym…”

Tabla de Contenidos: “…. -- See also -- Game of Atari using Deep Q Networks -- Getting ready -- How to do it... -- There's more... -- See also -- Policy gradients to play the game of Pong -- Getting ready -- How to do it... -- How it works... -- There's more... -- AlphaGo Zero -- See also -- Chapter 10: Mobile Computation -- Introduction -- TensorFlow, mobile, and the cloud -- Installing TensorFlow mobile for macOS and Android -- Getting ready -- How to do it... -- How it works... -- There's more…”

“…You'll also get hands-on experience with popular Python libraries and cover examples of classical reinforcement learning, path planning for autonomous agents, and developing agents to autonomously play Atari games. Next, you'll learn to solve common and not-so-common challenges in natural computing using neuroevolution-based algorithms. …”

“…You'll explore TensorFlow and OpenAI Gym to implement a deep learning RL agent that can play an Atari game. In addition to this, you will learn how to tune and configure RL algorithms and parameters by building agents for different kinds of games. …”

“…., producer of the first commercially successful personal computer, wrote a series of electronics books for Radio Shack that sold more than seven million copies, and designed the music synthesizer circuit that became known as the infamous Atari Punk Console. All this came before he started consulting for NASA's Goddard Space Flight Center, and NOAA's famous Mauna Loa Observatory, and earning the prestigious Rolex Award. …”

“…He started his career in game programming in 1982 at Atari Games, Inc., where he designed and programmed the classic arcade game Crystal Castles. …”

“…He is a lifelong video game aficionado who started with the Atari 2600 and never looked back. He enjoys music, reading, and sports in his spare time…”

“…As you delve into newly evolved areas of reinforcement learning, you'll gain an understanding of state-of-the-art algorithms that are the main components behind popular games Go, Atari, and Dota. By the end of the book, you will be well-versed with the theory of deep learning along with its real-world applications. …”

“…What you will learn Understand the theory and concepts behind modern Reinforcement Learning algorithms Code state-of-the-art Reinforcement Learning algorithms with discrete or continuous actions Develop Reinforcement Learning algorithms and apply them to training agents to play computer games Explore DQN, DDQN, and Dueling architectures to play Atari's Breakout using TensorFlow Use A3C to play CartPole and LunarLander Train an agent to drive a car autonomously in a simulator Who this book is for Data scientists and AI developers who wish to quickly get started with training effective reinforcement learning models in TensorFlow will find this book very useful. …”

“…He is a lifelong video game aficionado who started with the Atari 2600 and never looked back. He enjoys music, reading, and sports in his spare time…”

“…What you will learn Develop an agent to play CartPole using the OpenAI Gym interface Discover the model-based reinforcement learning paradigm Solve the Frozen Lake problem with dynamic programming Explore Q-learning and SARSA with a view to playing a taxi game Apply Deep Q-Networks (DQNs) to Atari games using Gym Study policy gradient algorithms, including Actor-Critic and REINFORCE Understand and apply PPO and TRPO in continuous locomotion environments Get to grips with evolution strategies for solving the lunar lander problem Who this book is for If you are an AI researcher, deep learning user, or anyone who wants to learn reinforcement learning from scratch, this book is for you. …”

“…You'll also learn how to use Deep Q-Networks to complete Atari games, along with how to effectively implement policy gradients. …”

“…What you will learn Understand core RL concepts including the methodologies, math, and code Train an agent to solve Blackjack, FrozenLake, and many other problems using OpenAI Gym Train an agent to play Ms Pac-Man using a Deep Q Network Learn policy-based, value-based, and actor-critic methods Master the math behind DDPG, TD3, TRPO, PPO, and many others Explore new avenues such as the distributional RL, meta RL, and inverse RL Use Stable Baselines to train an agent to walk and play Atari games Who this book is for If you're a machine learning developer with little or no experience with neural networks interested in artificial intelligence and want to learn about reinforcement learning from scratch, this..…”

“…Jonathan Harbour is a writer and instructor whose love for computers and video games dates back to the Commodore PET and Atari 2600 era. He has a Master’s in Information Systems Management. …”