FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning

Marvin Alles, Nutan Chen, Patrick van der Smagt, Botond Cseke·May 20, 2025

Summary

FlowQ, an offline reinforcement learning technique, enhances efficiency through energy-guided flow matching. It learns a conditional velocity field, approximating an energy-guided probability path as Gaussian, making it effective for tasks with combined data and energy functions. FlowQ scales with Q-function guidance, addressing distributional shifts in offline datasets. Optimized hyperparameters improve performance across various tasks, with specific values benefiting locomotion, manipulation, and navigation. Figures and tables demonstrate FlowQ's superior performance in D4RL locomotion and adroit environments compared to other methods.

Introduction
Background
Overview of offline reinforcement learning (RL)
Challenges in offline RL, particularly distributional shifts
Importance of energy functions in RL tasks
Objective
To introduce FlowQ, an offline RL technique that improves efficiency through energy-guided flow matching
To explain how FlowQ learns a conditional velocity field and approximates an energy-guided probability path
To highlight the scalability of FlowQ with Q-function guidance
Method
Data Collection
Techniques for collecting offline data
Importance of diverse and representative datasets
Data Preprocessing
Methods for preprocessing data to enhance learning
Handling distributional shifts in offline datasets
Learning Process
Overview of how FlowQ learns a conditional velocity field
Approximation of an energy-guided probability path using Gaussian distribution
Integration of Q-function guidance for scalability
Results
Performance Evaluation
Comparison of FlowQ with other offline RL methods
Metrics used for performance evaluation
Case Studies
Detailed analysis of FlowQ's performance in locomotion tasks
Examination of FlowQ's effectiveness in manipulation tasks
Insights into FlowQ's application in navigation tasks
Optimization
Hyperparameter Tuning
Importance of hyperparameter optimization in RL
Specific hyperparameters that benefit FlowQ's performance
Performance Improvement
Impact of optimized hyperparameters on FlowQ's performance
Case studies demonstrating performance gains
Conclusion
Summary of FlowQ's contributions
Future Directions
Potential areas for further research and development
Integration of FlowQ with emerging RL techniques
Basic info
papers
robotics
machine learning
artificial intelligence
Advanced features
Insights
In what ways does FlowQ address distributional shifts in offline datasets?
How does FlowQ's performance in D4RL locomotion and adroit environments compare to other methods?
How does FlowQ utilize energy-guided flow matching to enhance efficiency in offline reinforcement learning?
What are the optimized hyperparameters that improve FlowQ's performance in locomotion, manipulation, and navigation tasks?