  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.
