Year: 2005
Type: article
Abstract: Even in absence of external reward, babies and scientists and others explore their world.Using some sort of adaptive predictive world model, they improve their ability to answer questions such as: what happens if I do this or that?They lose interest in both the predictable things and those predicted to remain unpredictable despite some effort.We can design curious, self-motivated robots that do the same.The author's old basic principles for doing so: a reinforcement learning (RL) controller is rewarded whenever its action sequences result in predictor errors (1990), or, more generally, predictor improvements (1991).We briefly review the history of these ideas.
Author: Jürgen Schmidhuber
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2783159924