Curiosity-Driven AI Outperforms Reward Maximizers

An algorithm designed to seek out violations of its own model's predictions, treating surprise as interesting rather than as failure, can solve problems that typical reinforcement-learning reward-maximizers cannot, mirroring the insatiable curiosity that drives children to actively experiment on the world rather than passively await data.