  Interactive reinforcement learning can effectively facilitate the agent
training via human feedback. However, such methods often require the human
teacher to know what is the correct action that the agent should take. In other
words, if the human teacher is not always reliable, then it will not be
consistently able to guide the agent through its training. In this paper, we
propose a more effective interactive reinforcement learning system by
introducing multiple trainers, namely Multi-Trainer Interactive Reinforcement
Learning (MTIRL), which could aggregate the binary feedback from multiple
non-perfect trainers into a more reliable reward for an agent training in a
reward-sparse environment. In particular, our trainer feedback aggregation
experiments show that our aggregation method has the best accuracy when
compared with the majority voting, the weighted voting, and the Bayesian
method. Finally, we conduct a grid-world experiment to show that the policy
trained by the MTIRL with the review model is closer to the optimal policy than
that without a review model.
