“…In just 24 sessions of one hour or less, Sams Teach Yourself Android Game Programming in 24 Hours will help you master mobile game development for Android 4. …”

Tabla de Contenidos: “…Sample Updates -- Exploration vs. Exploitation -- Multi-arm Bandit -- Regret: Measure of Quality of Exploration -- Epsilon Greedy Exploration -- Upper Confidence Bound Exploration -- Thompson Sampling Exploration -- Comparing Different Exploration Strategies -- Planning at Decision Time and Monte Carlo Tree Search -- AlphaGo Walk-Through -- Summary -- Chapter 10: Further Exploration and Next Steps -- Model-Based RL: Additional Approaches -- World Models -- Imagination-Augmented Agents (I2A) -- Model-Based RL with Model-Free Fine-Tuning (MBMF) -- Model-Based Value Expansion (MBVE) -- Imitation Learning and Inverse Reinforcement Learning -- Derivative-Free Methods -- Transfer Learning and Multitask Learning -- Meta-Learning -- Popular RL Libraries -- How to Continue Studying -- Summary -- Index…”

Tabla de Contenidos: “…The Monte Carlo tree search algorithm -- Minimax and game trees -- The Monte Carlo Tree Search -- The SARSA algorithm -- SARSA algorithm for mountain car problem in OpenAI gym -- Summary -- Chapter 6: Asynchronous Methods -- Why asynchronous methods? …”

Tabla de Contenidos: “…Find Classic Games for Cheap; eBay and Other Online Shopping; Garage Sales, Flea Markets, and Thrift Stores; Before the sale.; At the sale.; 2. …”

Tabla de Contenidos: “…New Generations with Some of the Same Old Problems1991: Sony and Nintendo?; The PC Becomes Gaming Mature; Games That Pushed Boundaries III; Epilogue; Game Over; Press Start to Continue; Appendix A; Appendix B; Bibliography; Photo Credits; About the Author; Color Insert; Back cover…”

“…Based on extensive research and more than 140 interviews, Replay includes insights from video game legends such as Atari founder Nolan Bushnell, Will Wright-the creator of The Sims-Doom designer John Romero, and Hironobu Sakaguchi of Final Fantasy fame. …”

Tabla de Contenidos: “…Intro -- ÍNDICE -- INTRODUCCIÓN -- ANÍS DEL MONO -- APPLE -- ATARI -- BACARDÍ -- BIC -- BMW -- BOMBAY SAPPHIRE -- BOSCH -- BP -- CAFÉ DE COLOMBIA -- CAIXABANK -- CAMEL -- CHUPA CHUPS -- CITROËN -- CONTINENTAL -- ENI -- FERRARI -- FRUIT OF THE LOOM -- GOODYEAR -- GOOGLE -- GUINNESS -- HEINEKEN -- HERMÈS -- JOHNNIE WALKER -- KELLOGG'S -- LACOSTE -- LAMBORGHINI -- LONGINES -- MAZDA -- MERCEDES-BENZ -- MICHELIN -- MITSUBISHI -- MONTBLANC -- NBA -- NIKE -- OSBORNE -- PACHA -- PLAYBOY -- QUIKSILVER -- RED BULL -- ROLEX -- SHELL -- STARBUCKS -- SUBARU -- THE NORTH FACE -- THE ROLLING STONES -- TÍO PEPE -- TOBLERONE -- UNILEVER -- YAMAHA -- CRÉDITOS DE LAS IMÁGENES…”

Tabla de Contenidos: “…The policy gradient theorem -- Actor-critic algorithm -- Advantage actor-critic algorithm -- n-step advantage actor-critic algorithm -- n-step returns -- Implementing the n-step return calculation -- Deep n-step advantage actor-critic algorithm -- Implementing a deep n-step advantage actor critic agent -- Initializing the actor and critic networks -- Gathering n-step experiences using the current policy -- Calculating the actor's and critic's losses -- Updating the actor-critic model -- Tools to save/load, log, visualize, and monitor -- An extension - asynchronous deep n-step advantage actor-critic -- Training an intelligent and autonomous driving agent -- Training and testing the deep n-step advantage actor-critic agent -- Training the agent to drive a car in the CARLA driving simulator -- Summary -- Chapter 9: Exploring the Learning Environment Landscape - Roboschool, Gym-Retro, StarCraft-II, DeepMindLab -- Gym interface-compatible environments -- Roboschool -- Quickstart guide to setting up and running Roboschool environments -- Gym retro -- Quickstart guide to setup and run Gym Retro -- Other open source Python-based learning environments -- StarCraft II - PySC2 -- Quick start guide to setup and run StarCraft II PySC2 environment -- Downloading the StarCraft II Linux packages -- Downloading the SC2 maps -- Installing PySC2 -- Playing StarCraftII yourself or running sample agents -- DeepMind lab -- DeepMind Lab learning environment interface -- reset(episode=-1, seed=None) -- step(action, num_steps=1) -- observations() -- is_running() -- observation_spec() -- action_spec() -- num_steps() -- fps() -- events() -- close() -- Quick start guide to setup and run DeepMind Lab -- Setting up and installing DeepMind Lab and its dependencies -- Playing the game, testing a randomly acting agent, or training your own! …”

Tabla de Contenidos: “…Trip Hawkins, founder, Electronic Arts -- Nolan Bushnell, cofounder, Atari -- Wild Bill Stealey, cofounder, MicroProse Software -- Tony Goodman, cofounder, Ensemble Studios -- Feargus Urquhart, cofounder, Obsidian Entertainment -- Tim Cain, cofounder, Troika Games -- Warren Spector, founder, Junction Point Studios -- Doug and Gary Carlston, cofounders, Brderbund Software -- Don Daglow, founder, Stormfront Studios -- John Smedley, cofounder, Verant Interactive -- Ken Williams, cofounder, Sierra On-Line -- Lorne Lanning, cofounder, Oddworld Inhabitants -- Chris Ulm, cofounder, Appy Entertainment -- Tobi Saulnier, founder, 1st Playable Productions -- Christopher Weaver, founder, Bethesda Softworks -- Jason Rubin, cofounder, Naughty Dog -- Ted Price, founder, Insomniac Games…”

Tabla de Contenidos: “…The United States Peace Corps in the early 1960s / Agnieszka Sobocinska -- The naive republic of aid : grassroots exceptionalism in humanitarian memoir / Emily Bauman -- 'Telegenically dead Palestinians' : cinema, news media and perception management of the Gaza conflicts / Shohini Chaudhuri -- The Unknown Famine : television and the politics of British humanitarianism / Andrew Jones -- European borderscapes : the management of migration between care and control / Pierluigi Musarò -- The role of aid agencies in the media portrayal of children in Za'atari refugee camp / Toby Fricker -- Selling the lottery to earn salvation : journalism practice, risk and humanitarian communication / Jairo Lugo-Ocando and Gabriel Andrade -- Consumption, global humanitarianism and childhood / Laura Suski -- Liking visuals and visually liking on Facebook : from starving children to satirical saviours / Rachel Tavernor -- The corporate karma carnival : offline and online games, branding and humanitarianism at the Roskilde Festival / Lene Bull Christiansen and Mette Fog Olwig…”

“…Understand each key aspect of a deep RL problem Explore policy- and value-based algorithms, including REINFORCE, SARSA, DQN, Double DQN, and Prioritized Experience Replay (PER) Delve into combined algorithms, including Actor-Critic and Proximal Policy Optimization (PPO) Understand how algorithms can be parallelized synchronously and asynchronously Run algorithms in SLM Lab and learn the practical implementation details for getting deep RL to work Explore algorithm benchmark results with tuned hyperparameters Understand how deep RL environments are designed Register your book for convenient access to downloads, updates, and/or corrections as they become available. …”

Tabla de Contenidos: “…-- Fundamental concepts -- Feature learning -- Deep learning algorithms -- Deep learning applications -- Speech recognition -- Object recognition and classification -- GPU versus CPU -- Popular open source libraries - an introduction -- Theano -- TensorFlow -- Keras -- Sample deep neural net code using Keras -- Summary -- Chapter 4: Unsupervised Feature Learning -- Autoencoders -- Network design -- Regularization techniques for autoencoders -- Denoising autoencoders -- Contractive autoencoders -- Sparse autoencoders -- Summary of autoencoders -- Restricted Boltzmann machines -- Hopfield networks and Boltzmann machines -- Boltzmann machine -- Restricted Boltzmann machine…”

Tabla de Contenidos: “…Dynamic programming -- Value iteration -- Policy iteration -- Solving the frozen lake problem -- Value iteration -- Policy iteration -- Summary -- Questions -- Further reading -- Chapter 4: Gaming with Monte Carlo Methods -- Monte Carlo methods -- Estimating the value of pi using Monte Carlo -- Monte Carlo prediction -- First visit Monte Carlo -- Every visit Monte Carlo -- Let's play Blackjack with Monte Carlo -- Monte Carlo control -- Monte Carlo exploration starts -- On-policy Monte Carlo control -- Off-policy Monte Carlo control -- Summary -- Questions -- Further reading -- Chapter 5: Temporal Difference Learning -- TD learning -- TD prediction -- TD control -- Q learning -- Solving the taxi problem using Q learning -- SARSA -- Solving the taxi problem using SARSA -- The difference between Q learning and SARSA -- Summary -- Questions -- Further reading -- Chapter 6: Multi-Armed Bandit Problem -- The MAB problem -- The epsilon-greedy policy -- The softmax exploration algorithm -- The upper confidence bound algorithm -- The Thompson sampling algorithm -- Applications of MAB -- Identifying the right advertisement banner using MAB -- Contextual bandits -- Summary -- Questions -- Further reading -- Chapter 7: Deep Learning Fundamentals -- Artificial neurons -- ANNs -- Input layer -- Hidden layer -- Output layer -- Activation functions -- Deep diving into ANN -- Gradient descent -- Neural networks in TensorFlow -- RNN -- Backpropagation through time -- Long Short-Term Memory RNN -- Generating song lyrics using LSTM RNN -- Convolutional neural networks -- Convolutional layer -- Pooling layer -- Fully connected layer -- CNN architecture -- Classifying fashion products using CNN -- Summary -- Questions -- Further reading -- Chapter 8: Atari Games with Deep Q Network -- What is a Deep Q Network? …”

Tabla de Contenidos: “…More Unix Philosophy: Ten Lesser Tenets; 7.1 Allow the user to tailor the environment; 7.2 Make operating system kernels small and lightweight; 7.3 Use lowercase and keep it short; 7.4 Save trees; 7.5 Silence is golden; 7.6 Think parallel; 7.7 The sum of the parts is greater than the whole; 7.8 Look for the 90-percent solution; 7.9 Worse is better; 7.10 Think hierarchically…”

Tabla de Contenidos: “…-- Policy representation -- Policy gradients -- The REINFORCE method -- The CartPole example -- Results -- Policy-based versus value-based methods -- REINFORCE issues -- Full episodes are required -- High gradients variance -- Exploration -- Correlation between samples -- PG on CartPole -- Results -- PG on Pong -- Results -- Summary -- Chapter 10 - The Actor-Critic Method -- Variance reduction -- CartPole variance -- Actor-critic -- A2C on Pong -- A2C on Pong results -- Tuning hyperparameters -- Learning rate -- Entropy beta -- Count of environments -- Batch size -- Summary -- Chapter 11 - Asynchronous Advantage Afctor-Critic -- Correlation and sample efficiency -- Adding an extra A to A2C -- Multiprocessing in Python -- A3C - data parallelism -- Results -- A3C - gradients parallelism -- Results -- Summary -- Chapter 12 - Chatbots Training with RL -- Chatbots overview -- Deep NLP basics -- Recurrent Neural Networks -- Embeddings -- Encoder-Decoder…”