TY - JOUR
AU - Wu, Hongjie
AB - Reinforcement learning has a demand for massive data in complex problems, which makes it infeasible to be applied to real cases where sampling is difficult. The key to coping with these few-shot problems is knowledge generalization, and related algorithms are often called few-shot reinforcement learning (FS-RL). However, there lacks a formal definition and comprehensive analyses of few-shot scenarios and FS-RL algorithms. Therefore, after giving a uniform definition, we categorize few-shot scenarios into two types. The first type pursues more professional performance, while the other one pursues more general performance. In the process of knowledge transfer, few-shot scenarios usually have an obvious tendency to some type of knowledge. Based on this, we divide FS-RL algorithms into two types: the direct transfer case and the indirect transfer case. Thereafter, existing algorithms are discussed under this classification. Finally, we discuss future directions of FS-RL from the aspect of both theory and application.
TI - Reinforcement Learning in Few-Shot Scenarios: A Survey
JF - Journal of Grid Computing
DO - 10.1007/s10723-023-09663-0
DA - 2023-06-01
UR - https://www.deepdyve.com/lp/springer-journals/reinforcement-learning-in-few-shot-scenarios-a-survey-C0ESc2nzk6
VL - 21
IS - 2
DP - DeepDyve
ER -