The accelerating complexity, scale, and adversarial nature of modern cyber environments have rendered traditional, human-centric cyber defense strategies increasingly insufficient. Autonomous cyber defense, particularly approaches grounded in reinforcement learning and simulation-based experimentation, has emerged as a promising paradigm capable of adapting to dynamic threats, reasoning under uncertainty, and responding at machine speed. This article presents a comprehensive, theory-driven research investigation into autonomous cyber defense systems, with a particular focus on reinforcement learning agents trained within cyber simulation environments. Drawing exclusively on the provided body of literature, the study synthesizes advances in cyber operations research gyms, autonomous agent design, reward shaping, adversarial robustness, and the emerging threat of poisoned or trojaned learning agents. The article methodologically integrates insights from foundational intrusion detection research, deep reinforcement learning, stochastic games, and graph-based reasoning to articulate a unified conceptual framework for autonomous cyber defense. Results are presented as an extensive descriptive analysis of observed patterns, theoretical behaviors, and empirical findings reported across prior studies, emphasizing both capabilities and vulnerabilities. The discussion critically interrogates the limitations of current approaches, including closed-world assumptions, dataset bias, reward misalignment, and susceptibility to adversarial manipulation, while also exploring counter-arguments and mitigation strategies. Finally, the article outlines future research directions, emphasizing trustworthy autonomy, causal reasoning, and zero-day threat mitigation in complex software ecosystems. By providing a deeply elaborated, publication-ready synthesis, this work aims to serve as a foundational reference for researchers and practitioners seeking to advance the state of autonomous cyber defense.

Autonomous cyber defense, reinforcement learning, cyber simulation, intrusion detection

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