Course Information

Course Name	Turkish	Derin Pekiştirmeli Öğrenme
	English	Deep Reinforcement Learning
Course Code	YZV 415E	Credit	Lecture (hour/week)	Recitation (hour/week)	Laboratory (hour/week)
Semester	7	3	3	-	-
Course Language		English
Course Coordinator		Sanem Sarıel Uzer
Course Objectives		To introduce main methods in deep reinforcement learning To be able to model decision making under uncertainty problems as Markov Decision Processes To be able to design deep neural networks for deep reinforcement learning applications To understand applications of deep reinforcement learning for autonomous systems
Course Description		Introduction to Reinforcement Learning, Markov Decision Processes, Dynamic Programming, Model Free Reinforcement Learning, Approximate Dynamic Programming and Reinforcement Learning, Deep Reinforcement Learning and Neural Networks, Exploration Strategies, Partially Observable Problems, Model Based Deep Reinforcement Learning, Applications for Autonomous Systems
Course Outcomes		Know and discuss the main problems, application areas and the techniques of deep reinforcement learning Model various decision making under uncertainty problems as Markov Decision Processes Design various types of convolutional, fully connected and recurrent neural networks for deep reinforcement learning Know and apply different optimization algorithms and exploration strategies for dep reinforcement learning Implement computer realizations of deep reinforcement learning applications
Pre-requisite(s)		( YZV 302 MIN. DD or YZV 302E MIN. DD ) or YZV 303E MIN. DD
Required Facilities
Other
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