Abstract Reinforcement learning enables robots learn the optimal action policy through the interaction with the environment, representing an important frontier direction in the field of robotics. In this paper, the formal modeling of robot task planning problem is briefly introduced, and the main methods of reinforcement learning are analyzed, including model-free reinforcement learning, model-based reinforcement learning and hierarchical reinforcement learning. The research progress in robot task planning based on reinforcement learning is explored. Various reinforcement learning methods and their applications are discussed as well. Finally, the key problems of reinforcement learning in robot applications are summarized, and the future research directions are prospected.
Fund:Natural Science Foundation of Anhui Province(No.2208085MF174), Industry-Academy-Research Innovation Fund of Ministry of Education of China(No.2021ZYA06004)
Corresponding Authors:
ZHANG Xiaoming, Ph.D., associate professor. His research interests include swarm intelligence and inte-lligent decision making.
About author:: GAO Shijie, master student. His research interests include robot task planning, reinforcement learning and knowledge graph.YAO Changyu, master student. His research interests include robot task planning and reinforcement learning.CHU Yu, master student. Her research interests include intelligent decision.PENG Shuo, master student. His research interests include intelligent decision.
ZHANG Xiaoming,GAO Shijie,YAO Changyu等. Reinforcement Learning and Its Application in Robot Task Planning: A Survey[J]. Pattern Recognition and Artificial Intelligence, 2023, 36(10): 902-917.
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