Deep Reinforcement Meta-Learning and Self-Organization in Complex Systems: Applications to Traffic Signal Control

Area of Science:

• Artificial Intelligence
• Complex Systems
• Control Theory

Background:

• Deep reinforcement learning (DRL) and meta-learning methods struggle with dynamic complex systems.
• Traffic signal control in simulated urban environments presents a challenging test case.

Purpose of the Study:

• To evaluate DRL and self-organizing approaches for adapting to dynamic complex systems.
• To contrast the limitations of deep learning with self-organization in control tasks.
• To establish the importance of robust baselines for meta-learning evaluation.

Main Methods:

• Simulated urban traffic environment for dynamic system analysis.
• Comparison of state-of-the-art meta-learning algorithms against self-organizing methods.
• Performance evaluation of meta-learning versus classical learning techniques.

Main Results:

• Self-organizing traffic signal control outperformed state-of-the-art meta-learning in specific scenarios.
• Meta-learning methods demonstrated significant improvement (1.5-2x) over classical methods.
• Deep learning exhibited general limitations in controlling complex, dynamic systems.

Conclusions:

• Complex systems, like urban traffic, are essential for developing and refining meta-learning.
• Rigorous testing against established methods in demanding settings is necessary for effective meta-learning.
• Self-organization offers a promising alternative for adaptive control in complex dynamic environments.