Preference-based Learning of Reward Function Features

Understand

Preference-based learning of reward functions, where the reward function is learned using comparison data, has been well studied for complex robotic tasks such as autonomous driving.

• Existing algorithms have focused on learning reward functions that are linear in a set of trajectory features.
• The features are typically hand-coded, and preference-based learning is used to determine a particular user's relative weighting for each feature.
• Designing a representative set of features to encode reward is challenging and can result in inaccurate models that fail to model the users' preferences or perform the task properly.