The accelerating scale, complexity, and adversarial sophistication of cyber threats have fundamentally challenged traditional, rule-based security mechanisms. Autonomous cyber defense has emerged as a promising paradigm capable of responding to attacks at machine speed, adapting to unknown threats, and operating continuously within dynamic network environments. Reinforcement learning has been positioned at the center of this paradigm due to its ability to learn optimal policies through interaction with an environment. However, deploying reinforcement learning in real-world cyber defense raises profound challenges related to robustness, explainability, causality, scalability, and adversarial manipulation. This article presents a comprehensive and theoretically grounded research synthesis that integrates hierarchical reinforcement learning, deception-based defense strategies, causality-driven policy discovery, and explainable artificial intelligence into a unified conceptual framework for autonomous cyber defense. Drawing strictly from established academic literature and simulation platforms, the study examines how reinforcement learning agents can be trained using realistic cyber ranges and attack simulators to generalize across novel threat scenarios while maintaining resilience against adversarial interference. Particular emphasis is placed on optimal stopping formulations for intrusion prevention, feedback-enabled cyber resilience, and graph-based representations of attack–defense interactions. The article advances the argument that next-generation autonomous defense systems must move beyond performance-centric optimization toward architectures that explicitly encode causal reasoning, strategic deception, and human-interpretable decision processes. Through extensive methodological elaboration and interpretive analysis, the work identifies key research gaps and outlines future directions for building trustworthy, adaptive, and operationally viable autonomous cyber defense systems capable of functioning within complex software ecosystems and critical infrastructure networks.

Autonomous cyber defense, reinforcement learning, explainable AI, hierarchical learning,

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