Review of Generative Reinforcement Learning Based on Sequence Modeling
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.Reinforce-ment 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 uncer-tain and unknown environments,and more efficiently transform sequence data into strategies for decision-making.Firstly,an in-troduction is given to reinforcement learning algorithms and sequence modeling methods,and the modeling process of data se-quences 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.
万方数据
维普
