The Effect of Modeling Human Rationality Level on Learning Rewards from Multiple Feedback Types

When inferring reward functions from human behavior (be it demonstrations, comparisons, physical corrections, or e-stops), it has proven useful to model the human as making noisy-rational choices, with a "rationality coefficient" capturing how much noise or entropy we expect to see in the human behavior. Many existing works have opted to fix this coefficient regardless of the type, or quality, of human feedback. However, in some settings, giving a demonstration may be much more difficult than answering a comparison query. In this case, we should expect to see more noise or suboptimality in demonstrations than in comparisons, and should interpret the feedback accordingly. In this work, we advocate that grounding the rationality coefficient in real data for each feedback type, rather than assuming a default value, has a significant positive effect on reward learning. We test this in experiments with both simulated feedback, as well a user study. We find that when learning from a single feedback type, overestimating human rationality can have dire effects on reward accuracy and regret. Further, we find that the rationality level affects the informativeness of each feedback type: surprisingly, demonstrations are not always the most informative -- when the human acts very suboptimally, comparisons actually become more informative, even when the rationality level is the same for both. Moreover, when the robot gets to decide which feedback type to ask for, it gets a large advantage from accurately modeling the rationality level of each type. Ultimately, our results emphasize the importance of paying attention to the assumed rationality level, not only when learning from a single feedback type, but especially when agents actively learn from multiple feedback types.