Statistical limits and conditional complexity in real-world reinforcement learning: a tutorial survey.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Robotics

Background:

• Reinforcement learning (RL) excels in simulations but struggles with real-world deployment.
• Key limitations include sample inefficiency, nonstationarity, partial observability, and the curse of dimensionality.
• These statistical challenges hinder scalability, reliability, and safety in practical RL systems.

Purpose of the Study:

• To systematically examine the fundamental statistical challenges limiting real-world RL.
• To review theoretical lower bounds and contemporary mitigation strategies.
• To provide an organizational synthesis of RL's practical deployment hurdles.

Main Methods:

• Reviewing theoretical lower bounds for core RL challenges.
• Surveying current mitigation strategies, including model-based methods and robust formulations.
• Analyzing how structural assumptions impact RL performance.

Main Results:

• Identified sample inefficiency, nonstationarity, partial observability, and curse of dimensionality as primary statistical barriers.
• Characterized fundamental limits imposed by these challenges.
• Presented a unified view of mitigation techniques.

Conclusions:

• Addressing these statistical challenges is crucial for advancing real-world RL.
• The study offers an interpretive framework for understanding RL limitations and solutions.
• It serves as a tutorial synthesis, organizing existing knowledge for practical application.