Person:

Li, Tony

Reinforcement learning can be an arduous and expensive process, but agents can learn a task faster and better with the help of one or more teacher agents. In the advice model framework, a teacher instructs by giving advice to the student during its learning. In budgeted learning, the teacher can only dispense advice a limited number of times, which is more realistic in many domains and doesn't require the continual attention of the teacher to the student. This thesis explores how to extend the budgeted student-teacher framework to multiple teachers on budgets, framing for student agents as well as keeping the possibility of extending to human students, which would open up new possibilities in a plethora of domains. I evaluate the performance of multiple teachers in the Pac-Man domain. I show that it is possible for multiple teacher agents to outperform the single best teacher, that adding ``bad" teachers can have a negative effect on the student's learning, and that certain combinations of teaching heuristics with the number of teachers can be more beneficial to learning than others. I also present a simple model with promising learning results which leverages multiple individual teachers rather than aggregating all of them into a single entity.