Closing the Gap Between SVRG and TD-SVRG with Gradient Splitting

Core Concepts

Utilizing gradient splitting to enhance TD learning with SVRG, achieving geometric convergence.

Abstract

The content discusses the fusion of Temporal Difference (TD) learning with Stochastic Variance Reduced Gradient (SVRG) method to improve performance in reinforcement learning. It introduces a new approach using gradient splitting to simplify the algorithm and achieve geometric convergence. Theoretical findings are supported by experiments, showcasing significant practical implications in reducing sample complexities and improving convergence speed.
Directory:

Abstract

Fusion of TD learning with SVRG for geometric convergence.

Introduction

Policy evaluation in reinforcement learning.

Motivation and Contribution

Addressing high complexity issues in previous results.

Problem Formulation

Defining discounted reward Markov Decision Process.

The TD-SVRG Algorithm

Modification of TD(0) method for geometric convergence.

Main Results

Convergence theorems for different settings of TD-SVRG.

Outline of the Analysis

Viewing TD learning as gradient splitting for improved analysis.

Experimental Results

Performance comparison of algorithms in finite sample setting.

Conclusions

Significance of reducing scaling with condition number for policy evaluation.

Stats

Our main result is a geometric convergence bound with a predetermined learning rate of 1/8, matching that achieved by SVRG in convex optimization settings.

Quotes

"Previous analyses require batch sizes impractical for straightforward problems."
"Our analysis leads to batch-sizes implementable in practice."

Key Insights Distilled From

Closing the gap between SVRG and TD-SVRG with Gradient Splitting

by Arsenii Must... at arxiv.org 03-22-2024

https://arxiv.org/pdf/2211.16237.pdf

Deeper Inquiries

How can the concept of gradient splitting be applied to other areas within reinforcement learning

The concept of gradient splitting, as applied in the study to improve convergence rates in TD learning, can be extended to other areas within reinforcement learning. One potential application is in policy optimization algorithms such as Proximal Policy Optimization (PPO) or Trust Region Policy Optimization (TRPO). By viewing the update process as a form of gradient splitting, researchers could potentially enhance the convergence properties and efficiency of these algorithms. Additionally, applying gradient splitting concepts to value-based methods like Deep Q-Networks (DQN) could lead to improved training stability and faster convergence.

What are the potential drawbacks or limitations of utilizing variance reduction techniques in TD updates

While variance reduction techniques offer significant benefits in terms of accelerating convergence rates and reducing computational complexity, there are some potential drawbacks or limitations when utilizing them in TD updates. One limitation is that implementing variance reduction techniques may introduce additional hyperparameters that need tuning, which can increase the complexity of algorithm implementation. Furthermore, variance reduction methods may require more memory and computational resources compared to standard TD learning approaches. Additionally, these techniques may not always guarantee improvements in performance across all problem domains or datasets.

How might the findings from this study impact real-world applications of reinforcement learning algorithms

The findings from this study have several implications for real-world applications of reinforcement learning algorithms. Firstly, the development of TD-SVRG with improved convergence rates and reduced sample complexities can significantly benefit industries using reinforcement learning for decision-making processes such as finance or robotics. The ability to achieve geometric convergence with predetermined parameters enhances the practicality and efficiency of implementing these algorithms in complex environments.
Moreover, the insights gained from this research can inform future advancements in reinforcement learning theory and algorithm design. By demonstrating how gradient splitting can be leveraged for better performance in policy evaluation tasks, researchers can explore similar strategies for optimizing other RL algorithms across various domains. Overall, these findings contribute towards making reinforcement learning more effective and applicable in diverse real-world scenarios by improving its scalability and performance metrics.