This video compares human participants in a discrete Markov Decision Process and artificial agents. Agents have intrinsic motivation based on novelty, a learning rate that is modulated by surprise, as well as a standard reward-based reinforcement learning module. Humans are best explained by a complex agent that is (nearly) model-free, but uses novelty for exploration.