Transformer models, such as GPT-3 and Swin Transformer, have gained significant attention for their success in natural language processing and computer vision prediction tasks. Now, researchers are exploring their potential in another crucial domain: sequential decision-making and reinforcement learning. In a recent survey published by a research team led by Muning WEN, the team examines the use of sequence models, particularly the Transformer, in addressing the challenges of sample efficiency, credit assignment, and partial observability that often plague decision-making processes. The survey categorizes the works based on how they utilize the Transformer and showcases their effectiveness and generalizability.
By leveraging the power of large-scale sequence models, the research team envisions the construction of a comprehensive decision model that can perform multiple sequential decision-making tasks. This potential development holds promise for real-world applications such as robotics, autonomous vehicles, and the industrial automation sector.
The survey presents various approaches employed by researchers to utilize sequence models like the Transformer in sequential decision-making. It explores methods that convert reinforcement learning problems into sequential forms, capitalizing on the capabilities of sequence models in specific reinforcement learning settings. Additionally, the survey highlights techniques that utilize diverse data to pre-train large-scale sequence models for various sequential decision-making tasks, drawing inspiration from the successes of natural language processing and computer vision.
Looking towards the future, the research team offers several potential avenues for further investigation into improving the effectiveness of large sequence models for sequential decision-making. These areas include theoretical foundations, network architectures, algorithms, and efficient training systems. By sparking interest and inspiring further exploration into this trending topic, the team hopes to drive advancements in the field and facilitate the development of practical solutions.
With the publication of this survey, researchers, practitioners, and industry professionals alike are encouraged to delve into the potential applications of sequence models, particularly the Transformer, in sequential decision-making. The implications of these advancements extend to a wide range of fields, promising advancements in automation, robotics, and beyond.
As the field of sequential decision-making continues to evolve, the findings presented in this survey provide valuable insights and serve as a foundation for future research. By pushing the boundaries of large-scale sequence models, researchers aim to unlock the full potential of these models in addressing complex decision-making challenges and driving advancements in various domains.
DOI: 10.1007/s11704-023-2689-5
