Reinforcement learning based on human-computer interaction

A novel interactive learning structure integrated with a reinforcement learning algorithm and human-computer interaction (HCI) is proposed. This interactive learning system can benefit from measurements of the distance between the current state and goal state via an operator's professional knowledge. Thus, the learning procedure is expected to be more efficient. A guess-number task is explored to evaluate the proposed learning system. Experimental results show that the learning efficiency and convergence rate are both increased compared with the normal reinforcement learning method.