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Found 3 papers, 2 papers with code
Learning Reward Functions from Diverse Sources of Human Feedback: Optimally Integrating Demonstrations and Preferences
As designing reward functions can be extremely challenging, a more promising approach is to directly learn reward functions from human teachers.
Learning Reward Functions by Integrating Human Demonstrations and Preferences
In a user study, we compare our method to a standard IRL method; we find that users rated the robot trained with DemPref as being more successful at learning their desired behavior, and preferred to use the DemPref system (over IRL) to train the robot.
Unsupervised Visuomotor Control through Distributional Planning Networks
While reinforcement learning (RL) has the potential to enable robots to autonomously acquire a wide range of skills, in practice, RL usually requires manual, per-task engineering of reward functions, especially in real world settings where aspects of the environment needed to compute progress are not directly accessible.
