TRAINING FOR FUTURE
Scientists have developed an artificial intelligence (AI)
technique that will teach robots and computer programmes to interact with a
human instructor and perform tasks for the army.
Researchers at the US Army
Research Laboratory and the University of Texas at Austin considered a specific
case where a human provides real-time feedback in the form of critique.
First introduced by
researchers as Training an Agent Manually via Evaluative Reinforcement (TAMER),
the team developed a new algorithm called Deep TAMER.
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.
The team considered
situations where a human teaches an agent how to behave by observing it and
providing critique, for example, “good job” or “bad job” — similar to the way a
person might train a dog to do a trick.
Many current techniques in
artificial intelligence require robots to interact with their environment for
extended periods of time to learn how to optimally perform a task.
During this process, the
agent might perform actions that may not only be wrong, like a robot running
into a wall for example, but catastrophic, like a robot running off the side of
a cliff.
Help from humans will
speed things up for the agents, and help them avoid potential pitfalls, said
Garrett Warnell, a researcher at the US Army Research Laboratory.
As the 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.
Deep-TAMER-trained agents
exhibited superhuman performance, besting both their amateur trainers and, on
average, an expert human Atari player.
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.
“The army of the future
will consist of soldiers and autonomous teammates working side-by-side,”
Warnell said.
“While both humans and
autonomous agents can be trained in advance, the team will inevitably be asked
to perform tasks, for example, search and rescue or surveillance, in new environments
they have not seen before,” he said.
“In these situations,
humans are remarkably good at generalising their training, but current
artificially- intelligent agents are not,” he added.
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