Consider an embedded agent with a self-modifying, Turing-equivalent policy that can change only through active self-modifications. How can we make sure that it learns to continually accelerate reward intake? Throughout its life the agent remains ready to undo any self-modification generated during any earlier point of its life, provided the reward per time since then has not increased, thus enforcing a lifelong successstory of self-modifications, each followed by long-term reward acceleration up to the present time. The stack-based method for enforcing this is called the success-story algorithm. It fully takes into account that early self-modifications set the stage for later ones (learning a learning algorithm), and automatically learns to extend self-evaluations until the collected reward statistics are reliable... a very simple but general method waiting to be re-discovered! Time permitting, I will also briefly discuss more recent mathematically optimal universal maximizers of lifelong reward, in particular, the fully self-referential Godel machine.
['Juergen Schmidhuber']
Lifelong credit assignment with the success-story algorithm