Offline prompt reinforcement learning method based on feature extraction.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Robotics

Background:

• Offline reinforcement learning (RL) benefits from combining Transformer and conditional strategies.
• Conventional RL agents process observations sequentially, unlike Transformers which receive series of observations.
• Transformers struggle with efficient feature extraction and out-of-distribution generalization in RL.

Purpose of the Study:

• To enhance real-time policy adjustment in offline RL using few-shot learning and prompt learning.
• To improve decision-making accuracy and generalization capabilities of RL agents.
• To address the limitations of Transformers in processing sequential RL data.

Main Methods:

• Utilized few-shot learning characteristics of pre-trained models.
• Integrated prompt learning to encode task information from trajectory samples.
• Segmented state information blocks within trajectories for feature extraction.
• Encoded segmented sequences into a GPT model for decision generation.

Main Results:

• The proposed method demonstrated superior performance compared to baseline methods.
• Achieved enhanced generalization to new environments and tasks.
• Significantly improved the stability and accuracy of agent decision-making.

Conclusions:

• The integration of prompt learning and sequence-based feature extraction effectively addresses challenges in offline RL.
• The approach offers a promising direction for developing more robust and adaptable RL agents.
• The method shows significant potential for real-world applications requiring accurate and stable decision-making.