  Reward function, as an incentive representation that recognizes humans'
agency and rationalizes humans' actions, is particularly appealing for modeling
human behavior in human-robot interaction. Inverse Reinforcement Learning is an
effective way to retrieve reward functions from demonstrations. However, it has
always been challenging when applying it to multi-agent settings since the
mutual influence between agents has to be appropriately modeled. To tackle this
challenge, previous work either exploits equilibrium solution concepts by
assuming humans as perfectly rational optimizers with unbounded intelligence or
pre-assigns humans' interaction strategies a priori. In this work, we advocate
that humans are bounded rational and have different intelligence levels when
reasoning about others' decision-making process, and such an inherent and
latent characteristic should be accounted for in reward learning algorithms.
Hence, we exploit such insights from Theory-of-Mind and propose a new
multi-agent Inverse Reinforcement Learning framework that reasons about humans'
latent intelligence levels during learning. We validate our approach in both
zero-sum and general-sum games with synthetic agents and illustrate a practical
application to learning human drivers' reward functions from real driving data.
We compare our approach with two baseline algorithms. The results show that by
reasoning about humans' latent intelligence levels, the proposed approach has
more flexibility and capability to retrieve reward functions that explain
humans' driving behaviors better.
