Optimizing Hybrid AI Models with Reinforcement Learning for Complex Problem Solving
DOI:
https://doi.org/10.22399/ijcesen.2483Keywords:
Hybrid AI models, Reinforcement learning, Deep reinforcement learning, Optimization, Complex problem-solving, Genetic algorithmsAbstract
Hybrid AI models have gained significant attention due to their ability to combine the strengths of multiple artificial intelligence techniques, such as deep learning, evolutionary algorithms, and reinforcement learning (RL), to solve complex, real-world problems. This research explores the optimization of hybrid AI models with reinforcement learning to enhance their problem-solving capabilities in diverse domains, including robotics, healthcare, and autonomous systems. The proposed methodology integrates deep reinforcement learning (DRL) with genetic algorithms (GA) and neural networks to create adaptive models capable of learning from both supervised data and interactive environments. Through this integration, the hybrid models can optimize their decision-making processes over time, balancing exploration and exploitation to maximize performance.
The optimization process involves tuning the parameters of the reinforcement learning agent, such as the learning rate, discount factor, and exploration-exploitation ratio, to achieve the best possible outcome. Experimental results demonstrate that the hybrid AI model outperforms traditional single-algorithm approaches in terms of efficiency and accuracy. Specifically, in a robotic task optimization problem, the hybrid model achieved a 25% improvement in task completion time compared to standalone deep learning models. In a healthcare diagnosis scenario, the hybrid model showed a 15% increase in diagnostic accuracy, significantly reducing false positives and negatives. Furthermore, the optimization led to a 30% reduction in the training time compared to models that did not incorporate reinforcement learning.
The findings indicate that combining reinforcement learning with other AI techniques can significantly enhance the adaptability, efficiency, and problem-solving abilities of AI models. This research provides a foundation for developing more sophisticated hybrid AI systems for complex, dynamic environments.
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