Students who successfully complete this course are expected to acquire the knowledge of:

• the fundamentals of reinforcement learning
• Markov Decision Processes
• model-free prediction
• model-free control
• value function approximation
• policy gradient methods
• exploration and exploitation

At the end of the course, students will be able to pursue graduate studies in reinforcement learning related research areas.