AI News, New algorithms to train robots

New algorithms to train robots

It is an extension of TAMER that uses deep learning -- a class of machine learning algorithms that are loosely inspired by the brain to provide a robot the ability to learn how to perform tasks by viewing video streams in a short amount of time with a human trainer.

Warnell said the researchers extended earlier work in this field to enable this type of training for robots or computer programs that currently see the world through images, which is an important first step in designing learning agents that can operate in the real world.

As a first step, the researchers demonstrated Deep TAMER's success by using it with 15 minutes of human-provided feedback to train an agent to perform better than humans on the Atari game of bowling -- a task that has proven difficult for even state-of-the-art methods in artificial intelligence.

Within the next one to two years, researchers are interested in exploring the applicability of their newest technique in a wider variety of environments: for example, video games other than Atari Bowling and additional simulation environments to better represent the types of agents and environments found when fielding robots in the real world.

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