Often, many of these studies use a variety of retro games, such as for Atari 2600 (Atari Inc., Sunnyvale, CA, USA) or the original Nintendo Entertainment System (NES) (Nintendo Co., Ltd., Kyoto, Japan), to demonstrate the generalisability of an AI agent to play many games and optimise actions based on minimal coding that is unique to each game, such as to know where the score is shown (see Figure 1A). Here, I will provide: (1) an overview of observations where AI agents are perhaps not being fairly evaluated relative to humans, (2) a potential approach for making this comparison more appropriate, and (3) highlight some important recent advances in video game play provided by AI agents. The goal is rather to look at adaptive reasoning and strategies produced by AI agents that may replicate human approaches or even result in strategies not previously produced by humans. If the “finding” was merely that AI agents have superhuman reaction times and precision, none would be surprised. However, unlike strategy games, such as chess and Go, video games also rely heavily on sensorimotor precision. However, humans cannot match the fine precision of the timed actions of AI agents in games such as StarCraft, build orders take the place of chess opening gambits. Video games are sometimes used as environments to evaluate AI agents’ ability to develop and execute complex action sequences to maximize a defined reward.
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