Abstract
Reinforcement learning agents can learn to solve
sequential decision tasks by interacting with the
environment. Human knowledge of how to solve
these tasks can be incorporated using imitation
learning, where the agent learns to imitate human
demonstrated decisions. However, human guidance
is not limited to the demonstrations. Other types of
guidance could be more suitable for certain tasks
and require less human effort. This survey provides
a high-level overview of five recent learning frameworks that primarily rely on human guidance other
than conventional, step-by-step action demonstrations. We review the motivation, assumption, and
implementation of each framework. We then discuss possible future research directions