Abstract

Autonomous Intelligent Systems (AIS) have emerged as a transformative domain within artificial intelligence, enabling machines to perceive, reason, act, and learn in dynamic environments with minimal human intervention. Among the leading enabling technologies, Deep Reinforcement Learning (DRL) combines reinforcement learning’s reward‑based decision making with deep learning’s powerful representation learning, facilitating the development of agents that can optimize long‑term performance even under complex constraints. This paper explores the integration of DRL techniques and architectural frameworks that underpin contemporary AIS, tracing key developments, architectural paradigms, and the challenges that persist in real‑world applications. Through systematic analysis, we investigate canonical DRL approaches—including Deep Q‑Networks (DQN), Policy Gradient Methods, Actor‑Critic models, and hierarchical frameworks—highlighting their suitability across navigation, robotics, autonomous vehicles, and real‑time decision systems.

We present a comprehensive methodology emphasizing environment modeling, state representation, reward design, network architecture selection, policy optimization, and evaluation techniques. Simulation studies demonstrate the comparative performance of various DRL architectures in benchmark tasks like continuous control and multi‑agent coordination. Results indicate that hybrid architectures combining hierarchical learning, experience replay with prioritized sampling, and attention‑based state features significantly improve stability and convergence speed. This work further discusses the limitations of current DRL applications—such as sample inefficiency, safety concerns, sparse reward landscapes, and transferability to real‑world scenarios—and outlines mitigation strategies including imitation learning, curriculum learning, and reward shaping.

Our contribution lies in synthesizing multi‑disciplinary insights to offer design principles and evaluation criteria for AIS powered by DRL, providing a foundation for future research and practical implementation. By advancing architectural frameworks and refining learning strategies, this paper offers substantive pathways toward more robust, scalable, and reliable autonomous systems.