Student/Teacher Advising through Reward Augmentation
  Transfer learning is an important new subfield of multiagent reinforcement
learning that aims to help an agent learn about a problem by using knowledge
that it has gained solving another problem, or by using knowledge that is
communicated to it by an agent who already knows the problem. This is useful
when one wishes to change the architecture or learning algorithm of an agent
(so that the new knowledge need not be built "from scratch"), when new agents
are frequently introduced to the environment with no knowledge, or when an
agent must adapt to similar but different problems. Great progress has been
made in the agent-to-agent case using the Teacher/Student framework proposed by
(Torrey and Taylor 2013). However, that approach requires that learning from a
teacher be treated differently from learning in every other reinforcement
learning context. In this paper, I propose a method which allows the
teacher/student framework to be applied in a way that fits directly and
naturally into the more general reinforcement learning framework by integrating
the teacher feedback into the reward signal received by the learning agent. I
show that this approach can significantly improve the rate of learning for an
agent playing a one-player stochastic game; I give examples of potential
pitfalls of the approach; and I propose further areas of research building on
this framework.
