TY - GEN
T1 - Particle Swarm Based Reinforcement Learning
AU - Duan, Jianyu
AU - Guo, Yanxiao
AU - Wang, Zhigang
AU - Ke, Liangjun
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - With the vigorous development of computer-related technology, the “perception + decision” paradigm of the combination of deep learning and reinforcement learning has become a research hotspot. Nowadays, deep reinforcement learning algorithms have been successfully applied to the fields of games, industry and commerce. However, deep reinforcement learning algorithms often fall into the dilemma of “exploration” and “exploitation”, and the effect of these algorithms is easily affected by the quality of hyperparameters. In order to make up for the defects mentioned above, this paper introduces the particle swarm based reinforcement learning framework (PRL). Compared with the standard reinforcement learning algorithms, this framework greatly improves the exploration ability and obtains better scores in a series of gym experimental tests.
AB - With the vigorous development of computer-related technology, the “perception + decision” paradigm of the combination of deep learning and reinforcement learning has become a research hotspot. Nowadays, deep reinforcement learning algorithms have been successfully applied to the fields of games, industry and commerce. However, deep reinforcement learning algorithms often fall into the dilemma of “exploration” and “exploitation”, and the effect of these algorithms is easily affected by the quality of hyperparameters. In order to make up for the defects mentioned above, this paper introduces the particle swarm based reinforcement learning framework (PRL). Compared with the standard reinforcement learning algorithms, this framework greatly improves the exploration ability and obtains better scores in a series of gym experimental tests.
KW - Particle swarm optimization
KW - Reinforcement learning
KW - Twin delayed deep deterministic policy gradients
UR - https://www.scopus.com/pages/publications/85149689481
U2 - 10.1007/978-981-19-9297-1_3
DO - 10.1007/978-981-19-9297-1_3
M3 - 会议稿件
AN - SCOPUS:85149689481
SN - 9789811992964
T3 - Communications in Computer and Information Science
SP - 27
EP - 36
BT - Data Mining and Big Data - 7th International Conference, DMBD 2022, Proceedings
A2 - Tan, Ying
A2 - Shi, Yuhui
PB - Springer Science and Business Media Deutschland GmbH
T2 - 7th International Conference on Data Mining and Big Data, DMBD 2022
Y2 - 21 November 2022 through 24 November 2022
ER -