Abstract

This book chapter explores the integration of Policy Optimization and Actor-Critic methods within complex decision-making frameworks, highlighting their critical role in enhancing autonomous systems and real-time decision processes. It delves into the theoretical foundations of these techniques, emphasizing their applications in dynamic environments with high-dimensional state and action spaces. By examining recent innovations and real-world barriers, the chapter provides a comprehensive understanding of the challenges and opportunities associated with scaling these methods. Special attention was given to emerging advancements in sample efficiency, robustness to uncertainty, and multi-agent interactions, essential for practical deployment in sectors such as robotics, healthcare, and finance. It explores future directions, including the potential for hybrid approaches and improved generalization across domains. This chapter contributes valuable insights into the ongoing evolution of reinforcement learning, offering a roadmap for researchers and practitioners working on advanced decision-making systems.

Introduction

The growing complexity of decision-making environments in autonomous systems has necessitated the development of advanced techniques capable of optimizing policies in dynamic and uncertain contexts [1-3]. At the core of these advancements lies Policy Optimization and Actor-Critic methods, which have garnered significant attention for their ability to handle large-scale, high-dimensional decision-making problems [4,5]. These approaches offer substantial benefits in applications ranging from robotics to healthcare, where systems must learn to adapt to ever-changing environments while maximizing long-term rewards [6-9]. The integration of these methods into complex decision-making frameworks allows for more efficient and robust decision processes, crucial for real-time operations in real-world settings [10].

Policy Optimization techniques focus on directly improving the policy by optimizing a performance objective [11]. By using algorithms such as Proximal Policy Optimization (PPO) or Trust Region Policy Optimization (TRPO), systems can learn optimal actions that maximize rewards in environments with uncertain dynamics [12,13]. These techniques are highly effective in environments where traditional value-based methodsstruggle due to high-dimensional state spaces and the need for continuous action selections [14]. The key advantage of policy optimization was its ability to directly handle continuous action spaces, making it particularly suited for applications like robotics and autonomous vehicles, where precise control was necessary [15,16].