Information-Theoretic Generalization Bounds for Batch Reinforcement Learning.

Area of Science:

• Machine Learning
• Artificial Intelligence
• Information Theory

Background:

• Batch reinforcement learning (RL) is crucial for learning from fixed datasets.
• Understanding generalization in batch RL with function approximation is a key challenge.
• Information-theoretic approaches offer powerful tools for analyzing learning algorithms.

Purpose of the Study:

• To analyze the generalization properties of batch RL using an information-theoretic lens.
• To derive novel generalization bounds for batch RL.
• To connect structural assumptions on value function spaces with conditional mutual information.

Main Methods:

• Utilizing conditional mutual information to derive generalization bounds.
• Analyzing the relationship between value function space properties and information-theoretic measures.
• Developing high-probability generalization bounds.

Main Results:

• Derived generalization bounds for batch RL based on conditional mutual information.
• Established a link between structural assumptions on value function spaces and conditional mutual information.
• Obtained a novel high-probability generalization bound for batch RL.

Conclusions:

• Information-theoretic analysis provides effective tools for understanding batch RL generalization.
• The derived bounds offer theoretical guarantees for batch RL algorithms.
• The findings contribute to the theoretical foundations of reinforcement learning and function approximation.