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检索条件"主题词=Games Programming"

共 5 条记录，以下是1-10 订阅

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Experiencing Aspects of games programming in an Introductory Computer Graphics Class 07

Experiencing Aspects of Games Programming in an Introductory...

引用

38th SIGCSE Technical Symposium on Computer Science Education

作者： Sung, Kelvin Shirley, Peter Rosenberg, Becky Reed Univ Washington Bothell WA USA

ISBN: (纸本)9781595933614

Our computer graphics (CC) programming class uses am. development as a means to help students understand CG concepts. Many students mistakenly thought this CG class was a games programming class. We present a simple framework for discussing games programming classes. Based oil the framework, the paper describes our efforts in integrating competencies associated with games programming into our CG programming class. Our results show that the resulting class maintains the integrity of the original CG class while allowing students to develop projects with more interesting games features.

关键词： Graphics programming games programming Curriculum

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The myths (and truths) of java games programming

The myths (and truths) of java games programming

引用

7th International Conference on Intelligent games and Simulation

作者： Davison, Andrew Prince Songkla Univ Dept Comp Engn Songkhla 90112 Thailand

ISBN: (纸本)9789077381311

This paper examines the commonly-expressed criticisms of Java as a games programming language: that's it's too slow, too high-level, prone to memory problems, too hard to install, not available on games consoles, not used in 'real' games, and not even considered a gaming platform by Sun Microsystems. All of these views are incorrect, aside from the console issue.

关键词： java games programming myths criticisms

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Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more 1st edition

引用

2018年

作者： Maxim Lapan

ISBN: (数字)9781788839303

ISBN: (纸本)9781788834247

This practical guide will teach you how deep learning (DL) can be used to solve complex real-world problems. About This Book • Explore deep reinforcement learning (RL), from the first principles to the latest algorithms • Evaluate high-profile RL methods, including value iteration, deep Q-networks, policy gradients, TRPO, PPO, DDPG, D4PG, evolution strategies and genetic algorithms • Keep up with the very latest industry developments, including AI-driven chatbots Who This Book Is For Some fluency in Python is assumed. Basic deep learning (DL) approaches should be familiar to readers and some practical experience in DL will be helpful. This book is an introduction to deep reinforcement learning (RL) and requires no background in RL. What You Will Learn • Understand the DL context of RL and implement complex DL models • Learn the foundation of RL: Markov decision processes • Evaluate RL methods including Cross-entropy, DQN, Actor-Critic, TRPO, PPO, DDPG, D4PG and others • Discover how to deal with discrete and continuous action spaces in various environments • Defeat Atari arcade games using the value iteration method • Create your own OpenAI Gym environment to train a stock trading agent • Teach your agent to play Connect4 using AlphaGo Zero • Explore the very latest deep RL research on topics including AI-driven chatbots In Detail Recent developments in reinforcement learning (RL), combined with deep learning (DL), have seen unprecedented progress made towards training agents to solve complex problems in a human-like way. Google's use of algorithms to play and defeat the well-known Atari arcade games has propelled the field to prominence, and researchers are generating new ideas at a rapid pace. Deep Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL tools and their limitations. You will evaluate methods including Cross-entropy and policy gradients, before applying them to real-world environments. Take on both the Atari set of vir

关键词： Reinforcement Learning Deep Learning Machine Learning Python games programming Unsupervised Learning Algorithms Artifical Intelligence

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Input - Vol 1 No 03 (1997)(Marshall Cavendish)(Gb)