Reinforcement Learning: Theory and Applications in AI

Reinforcement Learning: Theory and Applications in AI introduces you to the dynamic field of reinforcement learning (RL) and its impact on artificial intelligence. Learn the fundamental principles of RL, including reward systems, value functions, and policy optimization. Understand key algorithms such as Q-learning, deep Q-networks, and policy gradients. Gain insights into practical applications of RL in various domains, from robotics and gaming to finance and healthcare. By the end of this course, you’ll be equipped with the knowledge and skills to design and apply RL solutions to complex problems.