Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (11): 213-228.doi: 10.11896/jsjkx.231000037

• Artificial Intelligence • Previous Articles     Next Articles

Review of Generative Reinforcement Learning Based on Sequence Modeling

YAO Tianlei, CHEN Xiliang, YU Peiyi   

  1. College of Command and Control Engineering,Army Engineering University of PLA,Nanjing 210007,China
  • Received:2023-10-07 Revised:2024-03-15 Online:2024-11-15 Published:2024-11-06
  • About author:YAO Tianlei,born in 2000,postgra-duate.His main research interest is deep reinforcement learning.
    CHEN Xiliang,born in 1985,Ph.D,associate professor.His main research interests include command information system engineering and deep reinforcement learning,etc.   
  • Supported by:
    National Natural Science Foundation of China(62273356).

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Abstract: Reinforcement learning is a branch of machine learning on how to learn decisions,which is a sequential decision-making problem that involves repeatedly interacting with the environment to find the optimal strategy through trial and error.Reinforcement learning can be combined with generative models to optimize their performance,and is typically used to fine-tune generative models and improve their ability to create high-quality content.The reinforcement learning process can also be seen as a general sequence modeling problem,modeling the distribution on task trajectories,and generating a series of actions through pre-training to obtain a series of high returns.Based on modeling input information,generative reinforcement learning can better handle uncertain and unknown environments,and more efficiently transform sequence data into strategies for decision-making.Firstly,an introduction is given to reinforcement learning algorithms and sequence modeling methods,and the modeling process of data sequences is analyzed.The development status of reinforcement learning is discussed according to different neural network models used.Based on this,relevant methods combined with generative models are summarized,and the application of reinforcement learning methods in generative pre-training models is analyzed.Finally,the development status of relevant technologies in theory and application is summarized.

Key words: Artificial intelligence, Reinforcement learning, Neural network, Generative model, Sequence modeling

CLC Number: 

  • TP181
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