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…”

“…Atari's 1981 arcade hit Tempest was a “tube shooter” built around glowing, vector-based geometric shapes. …”

“…Volume 02 focuses on Retrogaming and HD Recorders, and features coverage of HDTV Recorders, The Atari2600 PC Project , Podcasting, Mouse Robot, Star Wars Robot Builders. …”

“…Deep RL involves training an “agent” to become adept in given “environments,” enabling algorithms to meet or surpass human-level performance on a diverse range of complex challenges, including Atari video games, the board game Go, and subtle hand-manipulation tasks. …”

“…Shareware Heroes is a comprehensive, meticulously researched exploration of an important and too-long overlooked chapter in video game history Shareware Heroes: The renegades who redefined gaming at the dawn of the internet takes listeners on a journey, from the beginnings of the shareware model in the early 1980s, the origins of the concept, even the name itself, and the rise of shareware's major players-the likes of id Software, Apogee, and Epic MegaGames-through to the significance of shareware for the "forgotten" systems-the Mac, Atari ST, Amiga-when commercial game publishers turned away from them. …”

“…It details the terminology and core concepts of reinforcement learning, illustrates how OpenAI Gym software incorporates those core concepts, and shows you how to code solutions for reinforcement learning problems present in simple mazes and complex Atari games. Requirements include basic Python programming experience, basic math knowledge, familiarity with the Linux and Bash terminal, and an understanding of how to use Jupyter notebooks. …”

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…”

Tabla de Contenidos:

“…In the past decade deep RL has achieved remarkable results on a range of problems, from single and multiplayer games–such as Go, Atari games, and DotA 2–to robotics. Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. …”

“…Deep RL involves training an “agent” to become adept in given “environments,” enabling algorithms to meet or surpass human-level performance on a diverse range of complex challenges, including Atari video games, the board game Go, and subtle hand-manipulation tasks. …”

Tabla de Contenidos: “…: George Lucas, Lucasfilm, and the Legends of Transtextual Authorship across the Star Wars Franchise / Han Leia Shot First: Transmedia Storytelling and the National Public Radio Dramatization of Star Wars / From Sequel to Quasi-Novelization: Splinter of the Mind's Eye and the 1970s Culture of Transmedia Contingency / Another Canon, Another Time: The Novelizations of the Star Wars Films / Franchising Empire: Parker Brothers, Atari, and the Rise of LucasArts / "You must feel the Force around you!"…”

“…Learn to build a fully-functional 2D game inspired by the 1979 Atari classic, Asteroids, using just HTML5, CSS and JavaScript. …”

“…Takes you through building a classic Atari style brick breaker game Provides you the basic knowledge for building games Teaches you how to script and design UI elements of the game…”

Tabla de Contenidos:

Tabla de Contenidos: “…Experience replay -- Target network -- Clipping rewards -- Understanding the algorithm -- Building an agent to play Atari games -- Double DQN -- Prioritized experience replay -- Dueling network architecture -- Summary -- Questions -- Further reading -- Chapter 9: Playing Doom with a Deep Recurrent Q Network -- DRQN -- Architecture of DRQN -- Training an agent to play Doom -- Basic Doom game -- Doom with DRQN -- DARQN -- Architecture of DARQN -- Summary -- Questions -- Further reading -- Chapter 10: The Asynchronous Advantage Actor Critic Network -- The Asynchronous Advantage Actor Critic -- The three As -- The architecture of A3C -- How A3C works -- Driving up a mountain with A3C -- Visualization in TensorBoard -- Summary -- Questions -- Further reading -- Chapter 11: Policy Gradients and Optimization -- Policy gradient -- Lunar Lander using policy gradients -- Deep deterministic policy gradient -- Swinging a pendulum -- Trust Region Policy Optimization -- Proximal Policy Optimization -- Summary -- Questions -- Further reading -- Chapter 12: Capstone Project - Car Racing Using DQN -- Environment wrapper functions -- Dueling network -- Replay memory -- Training the network -- Car racing -- Summary -- Questions -- Further reading -- Chapter 13: Recent Advancements and Next Steps -- Imagination augmented agents -- Learning from human preference -- Deep Q learning from demonstrations -- Hindsight experience replay -- Hierarchical reinforcement learning -- MAXQ Value Function Decomposition -- Inverse reinforcement learning -- Summary -- Questions -- Further reading -- Assessments -- Other Books You May Enjoy -- Index…”

Tabla de Contenidos: “…Unix and Other Operating System Philosophies; 9.1 The Atari Home Computer: Human engineering as art; 9.2 MS-DOS: Over seventy million users can't be wrong; 9.3 VMS: The antithesis of UNIX?…”

Tabla de Contenidos: “…Evolution strategies -- ES on CartPole -- Results -- ES on HalfCheetah -- Results -- Genetic algorithms -- GA on CartPole -- Results -- GA tweaks -- Deep GA -- Novelty search -- GA on Cheetah -- Results -- Summary -- References -- Chapter 17 - Beyond Model- Free - Imagination -- Model-based versus model-free -- Model imperfections -- Imagination-augmented agent -- The environment model -- The rollout policy -- The rollout encoder -- Paper results -- I2A on Atari Breakout -- The baseline A2C agent -- EM training -- The imagination agent -- The I2A model -- The Rollout encoder -- Training of I2A -- Experiment results -- The baseline agent -- Training EM weights -- Training with the I2A model -- Summary -- References -- Chapter 18 - AlphaGo Zero -- Board games -- The AlphaGo Zero method -- Overview -- Monte-Carlo Tree Search -- Self-play -- Training and evaluation -- Connect4 bot -- Game model -- Implementing MCTS -- Model -- Training -- Testing and comparison -- Connect4 results -- Summary -- References -- Book summary -- Other Books You May Enjoy -- Index…”

Tabla de Contenidos: “…Incremental Prediction: MC, TD, TD(λ) -- Incremental Control -- Semi-gradient N-step SARSA Control -- Semi-gradient SARSA(λ) Control -- Convergence in Functional Approximation -- Gradient Temporal Difference Learning -- Batch Methods (DQN) -- Linear Least Squares Method -- Deep Learning Libraries -- Summary -- Chapter 6: Deep Q-Learning -- Deep Q Networks -- Atari Game-Playing Agent Using DQN -- Prioritized Replay -- Double Q-Learning -- Dueling DQN -- NoisyNets DQN -- Categorical 51-Atom DQN (C51) -- Quantile Regression DQN -- Hindsight Experience Replay -- Summary -- Chapter 7: Policy Gradient Algorithms -- Introduction -- Pros and Cons of Policy-Based Methods -- Policy Representation -- Discrete Case -- Continuous Case -- Policy Gradient Derivation -- Objective Function -- Derivative Update Rule -- Intuition Behind the Update Rule -- REINFORCE Algorithm -- Variance Reduction with Reward to Go -- Further Variance Reduction with Baselines -- Actor-Critic Methods -- Defining Advantage -- Advantage Actor Critic -- Implementation of the A2C Algorithm -- Asynchronous Advantage Actor Critic -- Trust Region Policy Optimization Algorithm -- Proximal Policy Optimization Algorithm -- Summary -- Chapter 8: Combining Policy Gradient and Q-Learning -- Trade-Offs in Policy Gradient and Q-Learning -- General Framework to Combine Policy Gradient with Q-Learning -- Deep Deterministic Policy Gradient -- Q-Learning in DDPG (Critic) -- Policy Learning in DDPG (Actor) -- Pseudocode and Implementation -- Gym Environments Used in Code -- Code Listing -- Policy Network Actor (PyTorch) -- Policy Network Actor (TensorFlow) -- Q-Network Critic Implementation -- PyTorch -- TensorFlow -- Combined Model-Actor Critic Implementation -- Experience Replay -- Q-Loss Implementation -- PyTorch -- TensorFlow -- Policy Loss Implementation -- One Step Update Implementation…”

Tabla de Contenidos: “….; TPJ #5: Commodities; TPJ #6: Scrabble; TPJ #7: Spiderball; TPJ #8: The Coffee Cup Fiasco; TPJ #9: Drummer/Coder Wanted; TPJ #10: The Underwood Typewriter; TPJ #11: The Conspiracy; TPJ #12: The Atari Perl Cartridge; TPJ #13: Dance Remixes; TPJ #14: Outlook Not So Good…”

“…How to disassemble every last byte of an Atari game and how to bypass every classic form of copy protection on Apple ][. …”