The widespread adoption of Internet-connected technologies has greatly increased their vulnerability to cyber assaults. The increasing intricacy and changing nature of these dangers require flexible, adaptable, and expandable security systems. Machine learning, particularly deep reinforcement learning (DRL), has become a commonly suggested approach to tackle these difficulties. A very successful method known as deep reinforcing training (DRL) is created by combining deep learning with traditional reinforced teaching. DRL can handle complicated, dynamic, and especially multidimensional cyber defense issues. In the context of cyber security, this paper provides a thorough analysis of DRL techniques. The text examines different crucial elements, such as security approaches based on DRL for cyber-physical systems, ways for autonomously detecting intrusions, and simulations using multi-agent DRL-based game theory to develop defence plans against cyber-attacks. This paper offers comprehensive analyses and profound perspectives on potential research paths in cyber security that utilise DRL. This comprehensive analysis seeks to establish a solid basis and encourage further investigation into the possibilities of developing DRL techniques in tackling the increasing complexity of cyber security concerns.