Improving Sample Efficiency with Policy Gradient Variants

Abstract

This article examines methods for improving sample efficiency in policy gradient reinforcement learning, focusing on the comparative performance of baseline gradient formulations and optimized variants designed to reduce variance and stabilize update dynamics. The study employs controlled training environments to evaluate convergence behavior, adaptability to shifting task conditions, and consistency across repeated trials, providing a detailed assessment of how update constraints, advantage normalization, and deterministic policy structures influence learning efficiency. Results show that optimized policy gradient methods achieve target performance levels with fewer environment interactions, demonstrating smoother reward progression, lower susceptibility to oscillatory learning behavior, and faster recovery after environmental changes. These improvements directly translate to reduced computational expense and increased robustness in applied AI deployments, particularly in enterprise and distributed computational systems where data access costs, response latency, and operational stability are critical. The findings suggest that sample-efficient policy gradient variants form a practical foundation for scalable autonomous decision-making and long-term adaptive reinforcement learning in real-world, continuously operating systems.