Long-Term Feature Extraction via Frequency Prediction for Efficient Reinforcement Learning.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Robotics

Background:

• Sample efficiency is a major hurdle for deploying deep reinforcement learning (RL) in real-world applications.
• Current methods often predict future states but overlook structural information in sequential data.
• This structural information, crucial for long-term decision-making, is challenging to extract from the time domain.

Purpose of the Study:

• To introduce a novel approach for representation learning in RL by leveraging the frequency domain of state sequences.
• To theoretically demonstrate the link between state sequence structure, policy performance, and signal regularity.
• To propose a method that extracts long-term features by predicting the Fourier transform of future state sequences.

Main Methods:

• Developed State Sequences Prediction via Fourier Transform (SPF), a novel representation learning technique.
• Analyzed the frequency domain's suitability for extracting policy-relevant structural information from time series data.
• Utilized a recursive relationship for simplified implementation and theoretical guarantees.

Main Results:

• SPF effectively extracts underlying patterns from state sequences in the frequency domain.
• The method provides an upper bound on the performance difference between optimal and learned policies.
• Experiments show superior sample efficiency and performance compared to state-of-the-art RL algorithms.

Conclusions:

• Leveraging the frequency domain offers a powerful new perspective for RL representation learning.
• SPF enhances decision-making by capturing long-term structural information missed by time-domain methods.
• The proposed approach significantly improves sample efficiency and overall performance in RL tasks.