This course introduces reinforcement learning, covering key techniques like multi-armed bandits, Q-learning, and deep reinforcement learning. Students will learn decision-making under uncertainty and apply advanced methods like actor-critic models to real-world problems.

Course Objectives

This course introduces reinforcement learning, covering key techniques like multi-armed bandits, Q-learning, and deep reinforcement learning. Students will learn decision-making under uncertainty and apply advanced methods like actor-critic models to real-world problems.

Course Content

Multi-armed bandits, epsilon greedy, upper confidence bounds, thompson sampling, contextual bandits, markov decision process, dynamic programming, policy and value iteration, monte carlo methods, temporal difference, Q-learning, deep Q-learning, actor-critic models.

Course Coordinator

Dr. Öğretim Üyesi Alper Demir

Course Lecturer(s)

Dr. Öğretim Üyesi Alper Demir

Recommended or Required Reading

Richard S. Sutton, Andrew G. Barto. Reinforcement Learning: An Introduction. Bradford Books, 2nd ed., 2018.

Learning Outcomes

1. To understand reinforcement learning fundamentals and key concepts.
2. To apply core algorithms like Q-learning and deep Q-learning.
3. To analyze Markov decision processes and policy optimization.
4. To implement reinforcement learning solutions in real-world scenarios.

Assignments (3): 24%

Midterm: 25%

Final: 35%

Paper Presentation: 16%