Reinforcement Learning from Human Reward: Discounting in Episodic Tasks
Abstract
Several studies have demonstrated that teaching agents by human-generated reward can be a powerful technique. However, the algorithmic space for learning from human reward has hitherto not been explored systematically. Using model-based reinforcement learning from human reward in goal-based, episodic tasks, we investigate how anticipated future rewards should be discounted to create behavior that performs well on the task that the human trainer intends to teach. We identify a positive circuits problem with low discounting (i.e., high discount factors) that arises from an observed bias among humans towards giving positive reward. Empirical analyses indicate that high discounting (i.e., low discount factors) of human reward is necessary in goal-based, episodic tasks and lend credence to the existence of the positive circuits problem.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 13, 2012
- Accession Number
- AD1024591
Entities
People
- Peter Stone
- W. B. Knox
Organizations
- University of Texas at Austin