Automating the search for entirely new 'curiosity' algorithms

Driven by an innate curiosity, children pick up new skills as they explore the world and learn from their experience. Computers, by contrast, often get stuck when thrown into new environments. To get around this, engineers have tried encoding simple forms of curiosity into their algorithms with the hope that an agent pushed to explore will learn about its environment more effectively. An agent with a child's curiosity might go from learning to pick up, manipulate, and throw objects to understanding the pull of gravity, a realization that could dramatically accelerate its ability to learn many other things. Engineers have discovered many ways of encoding curious exploration into machine learning algorithms. A research team at MIT wondered if a computer could do better, based on a long history of enlisting computers in the search for new algorithms.