Theory of mind as inverse reinforcement learning

Abstract

We review the idea that Theory of Mind—our ability to reason about other people's mental states—can be formalized as inverse reinforcement learning. Under this framework, expectations about how mental states produce behavior are captured in a reinforcement learning (RL) model. Predicting other people’s actions is achieved by simulating a RL model with the hypothesized beliefs and desires, while mental-state inference is achieved by inverting this model. Although many advances in inverse reinforcement learning (IRL) did not have human Theory of Mind in mind, here we focus on what they reveal when conceptualized as cognitive theories. We discuss landmark successes of IRL, and key challenges in building human-like Theory of Mind.