Kernel-Based Function Approximation for Average Reward Reinforcement Learning: An Optimist No-Regret Algorithm
Sattar Vakili, Julia Olkhovskaya·October 30, 2024
Summary
The paper introduces an optimistic algorithm for kernel-based function approximation in average reward reinforcement learning, focusing on the undiscounted setting. It offers novel no-regret performance guarantees and a confidence interval for kernel-based prediction, enhancing analytical understanding in complex environments. The algorithm is computationally efficient, suitable for continuous operations like load balancing and stock market management, and builds on the theoretical foundation of linear and deep learning models. The text discusses advancements in reinforcement learning and bandit algorithms, covering topics such as posterior sampling with delayed feedback, concentration inequalities, linear Markov decision processes, deep reinforcement learning in continuous action spaces, Gaussian process bandit optimization, and kernel-based reinforcement learning.
Introduction
Background
Overview of reinforcement learning and its applications
Importance of kernel-based function approximation in complex environments
Objective
Aim of the research: developing an optimistic algorithm for average reward reinforcement learning
Contribution to the field: novel no-regret performance guarantees and confidence interval for kernel-based prediction
Method
Data Collection
Techniques for collecting data in reinforcement learning environments
Data Preprocessing
Methods for preprocessing data to enhance algorithm performance
Algorithm Design
Detailed description of the optimistic algorithm
Integration of linear and deep learning models for efficiency
Theoretical Foundations
Underlying principles and assumptions of the algorithm
Analysis of computational complexity and scalability
Performance Guarantees
No-Regret Analysis
Explanation of no-regret performance guarantees
How the algorithm ensures optimal performance over time
Confidence Interval for Prediction
Methodology for calculating confidence intervals
Significance in decision-making under uncertainty
Applications
Continuous Operations
Case studies in load balancing and stock market management
Real-World Scenarios
Examples demonstrating the algorithm's effectiveness in practical settings
Related Work
Reinforcement Learning Advancements
Overview of recent developments in reinforcement learning
Bandit Algorithms
Discussion on posterior sampling with delayed feedback
Concentration inequalities and their role in reinforcement learning
Linear Markov Decision Processes
Analysis of linear MDPs and their relevance to the algorithm
Deep Reinforcement Learning
Exploration of deep learning in continuous action spaces
Gaussian Process Bandit Optimization
Integration of Gaussian processes for optimization
Kernel-Based Reinforcement Learning
Comparison with other kernel-based approaches in reinforcement learning
Conclusion
Summary of Contributions
Future Directions
Potential areas for further research and development
Basic info
papers
machine learning
artificial intelligence
Advanced features