Reinforcement Learning Prog

Course description

Reinforcement Learning (RL) is a key approach for building AI systems that learn through interaction, experimentation, and feedback. In this course, students explore foundational methodologies of RL, learning to design agent–environment systems, defining rewards, and implementing policies. Students work with simulated RL environments and apply techniques such as value-based methods, Q-learning, Deep Q-Networks (DQN), and policy-based optimization. Through hands-on activities, students build, train, and evaluate reinforcement learning agents while examining core concepts such as exploration, exploitation, and reward shaping. By the end of the course, students are able to construct and refine reinforcement learning systems and understand how RL integrates within the broader machine learning landscape

Students registering for credit courses for the first time must declare a program at the point of registration. Declaring a program does not necessarily mean students must complete a program, individual courses may be taken for skill improvement and upgrading.

For more information, please contact Continuing Education