Boosting Feedback Efficiency of Interactive Reinforcement Learning by Adaptive Learning from Scores
Shukai Liu,Chenming Wu,Ying Li,Liangjun Zhang,Shukai Liu,Chenming Wu,Ying Li,Liangjun Zhang
Interactive reinforcement learning has shown promise in learning complex robotic tasks. However, the process can be human-intensive due to the requirement of a large amount of interactive feedback. This paper presents a new method that uses scores provided by humans instead of pairwise preferences to improve the feedback efficiency of interactive reinforcement learning. Our key insight is that sco...
