The course introduces students to the design of algorithms that enable machines to learn based on reinforcements. In contrast to supervised learning where machines learn from examples that include the correct decision and unsupervised learning where machines discover patterns in the data, reinforcement learning allows machines to learn from partial, implicit and delayed feedback. This is particularly useful in sequential decision making tasks where a machine repeatedly interacts with the environment or users. Applications of reinforcement learning include robotic control, autonomous vehicles, game playing, conversational agents, assistive technologies, computational finance, operations research, etc.
At the end of the course, students should have the ability to:
Model tasks as reinforcement learning problems
Identify suitable algorithms and apply them to different reinforcement learning problems