Reinforcement Learning: From Theory to Applications - Sinha, Kumari Priyanka (AG Volumes, 2024)
In artificial intelligence, reinforcement learning (RL) has become a dynamic and revolutionary paradigm that has the potential to enable intelligent decision-making in dynamic and complicated contexts. By investigating its surroundings at random and drawing on past experiences, RL keeps learning new things. Because of its many features—including self-improvement, online learning, and little programming effort—Reinforcement Learning has emerged as one of the most intelligent agents. Examine the current works that address innate difficulties and those that concentrate on a variety of applications. Because reinforcement learning is self-improving, web-based, and requires less programming work, it may function as an intelligent agent in core technologies. Even with the development of more reliable and effective algorithms, more work has to be done. Artificial intelligence concepts are becoming simpler and less universal due to the influence of reinforcement learning.