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Project Zelda 3D: The Development of Zelda 64 - Unseen64
"What happened to Zelda 64 and why Ocarina of Time is just a tiny part of their original project? Take some time and be prepared for one of the most epic legends ever: birth, development, death and rebirth of the Zelda 3D project for Nintendo 64, a game that was so ambitious that it was never really completed."
video  games  history  nintendo  zelda  oot 
10 hours ago by ddribin
SerpentAI/SerpentAI: Game Agent Framework. Helping you create AIs / Bots to play any game you own! BETA
SerpentAI - Game Agent Framework. Helping you create AIs / Bots to play any game you own! BETA
games  python  ai  automation 
12 hours ago by geetarista
The Mathematics of 2048: Counting States with Combinatorics
How many board configurations are there in the game of 2048? Let's estimate using combinatorics.
games  math 
12 hours ago by geetarista
Short Trip
> Short Trip: absurdly charming hand-drawn train journey; hold left or right to move
> https://twitter.com/waxy/status/912371126311034880
games  tram  4kids  interactive  game  design  illustration  art  animation  inspiration 
13 hours ago by jefftriplett
Gamasutra - The world is the genre: How devs can make
At VRDC Fall 2017, XEODesign chief and game designer Nicole Lazzaro outlines possible futures for VR/AR/MR games -- and walks devs through what they c
VR  games  videogames  simulation  gamelab 
15 hours ago by basemaly
[1709.06009] Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents
The Arcade Learning Environment (ALE) is an evaluation platform that poses the challenge of building AI agents with general competency across dozens of Atari 2600 games. It supports a variety of different problem settings and it has been receiving increasing attention from the scientific community, leading to some high-profile success stories such as the much publicized Deep Q-Networks (DQN). In this article we take a big picture look at how the ALE is being used by the research community. We show how diverse the evaluation methodologies in the ALE have become with time, and highlight some key concerns when evaluating agents in the ALE. We use this discussion to present some methodological best practices and provide new benchmark results using these best practices. To further the progress in the field, we introduce a new version of the ALE that supports multiple game modes and provides a form of stochasticity we call sticky actions. We conclude this big picture look by revisiting challenges posed when the ALE was introduced, summarizing the state-of-the-art in various problems and highlighting problems that remain open.
machine-learning  benchmarking  games  to-write-about  nudge-targets  consider:performance-measures 
19 hours ago by Vaguery

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