Reinforcement learning has its limitations, though. Agrawal notes that it often takes a huge amount of training to learn a task, and the process can be difficult if the feedback required isn’t immediately available. For instance, the method doesn’t work for computer games in which the benefits of certain behaviors isn’t immediately obvious. That’s where curiosity could help.

The researchers tried the approach, in combination with reinforcement learning, within two simple video games: Mario Bros., a classic platform game, and VizDoom, a basic 3-D shooter title.

In both games, the use of artificial curiosity made the learning process more efficient. In the 3-D game, for instance, instead of spending an excessive amount of time bumping into walls, the agent moved around its environment, learning to navigate more quickly. Even without any other reward, the agent was able to navigate both games surprisingly well. In Mario Bros. it learned to avoid getting killed because this lessened its ability to explore and learn about its environment.

A paper describing the research will be published at a major AI conference later this year.

Artificial curiosity has been an active area of research for some time. Pierre-Yves Oudeyer, a research director at the French Institute for Research in Computer Science and Automation, has pioneered, over the past several years, the development of computer programs and robots that exhibit simple forms of inquisitiveness.

“What is very exciting right now is that these ideas, which were very much viewed as ‘exotic’ by both mainstream AI and neuroscience researchers, are now becoming a major topic in both AI and neuroscience,” Oudeyer says.

The work could have real practical benefits. The UC Berkeley team is keen to test it on robots that use reinforcement learning to work out how to do things like grasps awkward objects. Agrawal says robots can waste a huge amount of time performing random gestures. When equipped with innate curiosity, such a robot should more quickly explore its surroundings and experiment with nearby objects, he says.

Brenden Lake, a research scientist at New York University who builds computational models of human cognitive capabilities, says the work seems promising. “Developing machines with similar qualities is an important step toward building machines that learn and think like people,” he said in an e-mail. “It’s very impressive that by using only curiosity-driven learning, the agent can learn to navigate a level in Mario. The agent doesn’t even look at the game score.”

At the same time, says Lake, the inquisitiveness demonstrated by the new program is actually pretty different from, say, that of a child. Humans tend to display a much deeper interest in their world, he says.

“It’s a very egocentric form of curiosity,” Lake says. “The agent is only curious about features of its environment that relate to its own actions. People are more broadly curious. People want to learn about the world in ways less directly tied to their own actions.”