Reinforcement Learning in Real-World Scenarios: Challenges, Applications, and Future Directions

Abstract

Reinforcement Learning (RL) has developed as a powerful machine learning paradigm that has achieved great success in a variety of applications such as gaming, robotics, and natural language processing. As academics become more interested in using RL in real-world contexts, they face new problems and complications. This work investigates the key issues of RL in real-world contexts, such as dealing with high-dimensional and continuous state-action spaces, as well as coping with partial observability via state estimation. It investigates the trade-offs between real-world experience and simulations, considering the expense and feasibility of getting real-world physical data for training. The importance of reward shaping in leading RL agents in real-world contexts is being researched. This paper discusses the limitations of RL in real-world applications, as well as potential future possibilities for developing the subject. Understanding these obstacles and opportunities will allow academics and practitioners to fully realize the potential of RL in tackling real-world problems and opening new paths for revolutionary applications.