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      GameDev.net and CRC Press have teamed up to bring a free ebook of content curated from top titles published by CRC Press. The freebook, Practices of Game Design & Indie Game Marketing, includes chapters from The Art of Game Design: A Book of Lenses, A Practical Guide to Indie Game Marketing, and An Architectural Approach to Level Design. The GameDev.net FreeBook is relevant to game designers, developers, and those interested in learning more about the challenges in game development. We know game development can be a tough discipline and business, so we picked several chapters from CRC Press titles that we thought would be of interest to you, the GameDev.net audience, in your journey to design, develop, and market your next game. The free ebook is available through CRC Press by clicking here. The Curated Books The Art of Game Design: A Book of Lenses, Second Edition, by Jesse Schell Presents 100+ sets of questions, or different lenses, for viewing a game’s design, encompassing diverse fields such as psychology, architecture, music, film, software engineering, theme park design, mathematics, anthropology, and more. Written by one of the world's top game designers, this book describes the deepest and most fundamental principles of game design, demonstrating how tactics used in board, card, and athletic games also work in video games. It provides practical instruction on creating world-class games that will be played again and again. View it here. A Practical Guide to Indie Game Marketing, by Joel Dreskin Marketing is an essential but too frequently overlooked or minimized component of the release plan for indie games. A Practical Guide to Indie Game Marketing provides you with the tools needed to build visibility and sell your indie games. With special focus on those developers with small budgets and limited staff and resources, this book is packed with tangible recommendations and techniques that you can put to use immediately. As a seasoned professional of the indie game arena, author Joel Dreskin gives you insight into practical, real-world experiences of marketing numerous successful games and also provides stories of the failures. View it here. An Architectural Approach to Level Design This is one of the first books to integrate architectural and spatial design theory with the field of level design. The book presents architectural techniques and theories for level designers to use in their own work. It connects architecture and level design in different ways that address the practical elements of how designers construct space and the experiential elements of how and why humans interact with this space. Throughout the text, readers learn skills for spatial layout, evoking emotion through gamespaces, and creating better levels through architectural theory. View it here. Learn more and download the ebook by clicking here. Did you know? GameDev.net and CRC Press also recently teamed up to bring GDNet+ Members up to a 20% discount on all CRC Press books. Learn more about this and other benefits here.

Sevren

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  1. Hi everyone. I've been learning about Reinforcement learning for the past little bit in an attempt to learn how to create a agent that I could use in a game i.e driving a car around a track. I want to learn how to combine the Neural network architecture with RL such as Q-learning or SARSA.  Normally in Error- back propagation Neural Networks you have both input and a given target i.e xor pattern input is 0 0 or 1 1 or 0 1 1 0 and the target is either 0 or 1. This is given so it is easy for me to see where to plug in the values for my error back prop function. The problem for me now is given only the state variables  in my testing problem of Mountain car or pendulum how do I go about using Error- back propagation?  Since I first want to build an agent that solves Mountain car as a test Is this the right set of steps? S =[-0.5; 0] as the inital state ( input into my neural network) create network (2, X-hidden units,3) -> 2 inputs position and velocity  and either 1 ouput or 3 outputs corresponding to actions, with Hidden activation function is sigmoid(tanh) and output is purelin   Now run the state values for position and velocity into the network (Feed forward) and get 3 Q values as output, it's 3 outputs as that is how many actions I have.    select an action A using e-greedy, either a random one or the best Q-value giving me which action to choose from this state.   Execute action A for the problem and receive new state S' and reward   Run S' through the neural network and obtain Q S' values Now I guess I need to compute a target value... given Q-learning where Q(s,a) = Q(s,a)+alpha*[reward+gamma* MAX Q(s',a') -Q(s,a)] I think my Target output is calculated using:  QTarget=reward+gamma*MAX Q(s',a') right? So that means now i choose the max Q-value from step  5 and plug it into the QTarget  equation  Do I calculate an Error again like in the original backprop algo? So Error=QTarget-Q(S,A) ? and now resume normal Neural Network backprop weight updates? Thanks, Sevren