How Has AI & Machine Learning Evolved Gaming?

Have you ever wondered how complex games like War of Warcraft or Dota really work? In games where you’re literally in another virtual world, basic programming doesn’t work. You have to use AI and machine learning to develop the game so that it is realistic for the player.

The hard core video games we have today deal with multiple players in real time. In such a situation, where everyone is making a different move in a virtual space that is so vast, you need AI to allow the game to function smoothly. After all, every game will have certain characters, objects and barriers that aren’t real players don’t operate. 

How to Apply AI & Machine Learning to Develop Games?

In order to train an AI to play a game, the machine learning algorithm you need to use here is deep reinforcement learning. This involves placing the AI in a simulated environment. Here, it learns on its own how to go about survival as it gets rewards on every correct move. More complicated the game, the greater inputs you’ll need to train the AI.

How NetHack Will be Evolving with AI?

NetHack is a game that was developed back in 1987. It has more than 50 dungeon levels where players need to get a magical amulet by fighting monsters with wands and weapons. With time, the makers have developed the environment and architecture of the game to train AI more quickly.

This has given the base for the recent NetHack Challenge. Here, you need to develop an AI to win or get the highest score in one of the toughest games ever. Facebook is already willing to fund AI researchers for the same.  By the end of the year, we’ll know what new levels can be reached with AI in a game like this.

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