Policy Gradient Methods in Deep Reinforcement Learning

Policy gradient (PG) methods are a fundamental component of deep reinforcement learning (DRL), particularly effective in continuous and high-dimensional control tasks. This paper presents a structured review of PG algorithms, tracing their development from basic Monte Carlo methods like REINFORCE to advanced techniques such as asynchronous advantage actor-critic (A3C), trust region policy optimization (TRPO), proximal policy optimization (PPO), deep deterministic policy gradient (DDPG), and soft actor-critic (SAC). These methods differ in terms of policy structure, optimization stability, and sample efficiency, addressing core challenges in policy learning through gradient-based updates. In addition, this review explores the application of PG methods in real-world domains, including autonomous driving, financial portfolio management, and smart grid energy systems. These applications demonstrate PG methods capacity to operate under uncertainty and adapt to complex dynamic environments. However, limitations such as high variance, low sample efficiency, and instability in multi-agent and offline settings remain significant obstacles. The review concludes by outlining emerging research directions, including entropy-based exploration, model-based policy optimization, meta-learning, and Transformer-based sequence modeling. This work aims to offer theoretical insights and practical guidance to support the continued advancement and application of policy gradient methods in reinforcement learning.