Reinforcement Learning Algorithms and Applications in Healthcare and Robotics: A Comprehensive and Systematic Review.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Computational Science

Background:

• Reinforcement learning (RL) is a transformative AI paradigm for intelligent decision-making in dynamic environments.
• RL excels at sequential decision-making problems involving simultaneous sampling, evaluation, and feedback.
• RL techniques are increasingly applied across diverse domains due to their powerful solution development capabilities.

Purpose of the Study:

• To provide a comprehensive and systematic review of reinforcement learning algorithms and their applications.
• To explore the foundations of RL, examine algorithms in detail, and conduct a comparative analysis.
• To investigate key RL applications in robotics manipulation and healthcare, specifically cell growth problems.

Main Methods:

• Systematic literature review of reinforcement learning algorithms.
• Detailed examination and comparative analysis of various RL algorithms based on defined criteria.
• Exploration of RL applications in robotics (e.g., object grasping, autonomous learning) and healthcare (e.g., cell culture optimization, therapeutic development).

Main Results:

• RL algorithms demonstrate significant potential for enhancing precision and adaptability in robotic manipulation tasks.
• Reinforcement learning provides a data-driven approach to optimize cell culture growth and develop novel therapeutic solutions in healthcare.
• The review highlights the evolving landscape and interconnected potential of RL in diverse fields.

Conclusions:

• Reinforcement learning is a powerful tool for addressing complex decision-making challenges.
• RL applications in robotics and healthcare showcase its versatility and impact on scientific advancement.
• This review offers a foundational understanding and outlook on the future of reinforcement learning.