Machine Learning and Reinforcement Learning for Intelligent Systems: A Comprehensive Review of Predictive Modeling, Autonomous Control, and Causal Reasoning

Hoda A. Abdel*

doi:10.5281/zenodo.21212311

International Journal of AI and Advanced Computing

Volume 1 , Issue 2

Research Article • Open Access

Machine Learning and Reinforcement Learning for Intelligent Systems: A Comprehensive Review of Predictive Modeling, Autonomous Control, and Causal Reasoning

Hoda A. Abdel*

Pages 75-84

DOI: 10.5281/zenodo.21212311

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Abstract

The convergence of machine learning, deep learning, and reinforcement learning has fundamentally transformed intelligent systems across diverse domains, enabling predictive modeling, autonomous decision-making, and adaptive control in complex environments. This comprehensive review examines the application of machine learning and reinforcement learning algorithms across six interconnected domains: cybersecurity threat detection, photovoltaic system fault diagnosis, pharmaceutical drug delivery systems, environmental rainfall forecasting, autonomous navigation and control, and adaptive cybersecurity defense. Through a structured literature review and comparative synthesis of empirical studies and theoretical frameworks published between 2020 and 2026, this paper investigates the performance of supervised learning algorithms, including Decision Trees, Random Forest, Support Vector Machines, K-Nearest Neighbors, and Artificial Neural Networks, alongside reinforcement learning algorithms, including Proximal Policy Optimization, Soft Actor-Critic, and Deep Q-Networks, as well as emerging causal reinforcement learning paradigms. The review reveals that ensemble-based learning approaches consistently outperform single-model architectures in supervised learning tasks, with Random Forest achieving F1-scores of 97.5% in cybersecurity threat detection and 92.3% accuracy in pharmaceutical prediction. Neural network architectures demonstrate superior capability in capturing nonlinear interactions, with wide neural networks achieving 98.88% accuracy in photovoltaic fault classification and CNN-LSTM ensembles achieving 98.7% F1-score in cybersecurity applications. Reinforcement learning with adaptive curriculum learning achieves navigation success rates of 89% in complex dynamic environments, while reinforcement learning-based CAPTCHA defense systems achieve 97.7% accuracy in distinguishing human users from automated bots. Causal reinforcement learning frameworks demonstrate that incorporating structural causal knowledge can improve sample efficiency by 35-40% compared to model-free approaches, with federated natural policy gradient methods reducing communication complexity from O(d²) to O(d). The review identifies key challenges including data quality issues, model interpretability limitations, computational scalability constraints, generalization gaps across domains, and the need for standardized benchmarking frameworks. Future research directions include explainable AI methods for critical applications, federated learning architectures for privacy-preserving distributed modeling, hybrid physics-informed machine learning approaches, scalable causal reinforcement learning, and the integration of curriculum learning and domain randomization for robust real-world deployment. The findings provide a comprehensive algorithm selection framework and cross-domain comparative perspective, emphasizing that while deep learning excels in complex pattern recognition tasks, ensemble methods offer robust performance with greater interpretability, and reinforcement learning provides a unifying paradigm for sequential decision-making under uncertainty.

Keywords

Machine Learning, Reinforcement Learning, Predictive Modeling, Deep Learning, Causal Reinforcement Learning, Cybersecurity, Photovoltaic Systems, Autonomous Navigation, Ensemble Learning,

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