The exponential growth of the Internet of Things (IoT) and cyber-physical systems, coupled with the increasing frequency of accidental and malicious cyber events, has created a complex, ambiguous, and nonlinear security environment. Traditional cyber resilience algorithms, often static and nonadaptive, struggle to keep pace with the dynamic nature of modern cyber threats. As such, there is an urgent need for advanced, predictive solutions that can learn and adapt in real time. Recent advances in artificial intelligence (AI) and network-optimized architectures (NOA) have shown significant promise addressing cyber challenges, and reinforcement learning (RL) has been effectively employed to incrementally build and optimize neural network classifiers. We leverage this approach to potentially enable systems to better adapt to new data and evolving threats. We propose this can remove/reduce the need for excessive retraining, helping enable a continuous learning-based cyber defense posture. We therefore integrate RL into neural NOA enabling an automated tuning and introspective improvement of neural networks, continuously tailoring cyber resilience, and defending against known/unknown cyber events/challenges. Furthermore, we included a cyber resilience policy learning framework and abstract problem embedding to transfer learned policies for enhancing the system’s ability to introspectively defend against novel cyberattacks. Lastly, we describe architecturally how to continually enhance neural network architectures for providing a more resilient cyber defense posture supporting withstanding both known and unknown threats.