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ECE 617 Reinforcement Learning 3.0 Credits

Reinforcement Learning (RL) has emerged as a powerful paradigm for creating intelligent, autonomous agents capable of learning from their interactions with the environment. This course provides a comprehensive understanding of key theoretical concepts, and students will learn about the core challenges and approaches, including generalization and exploration. Through a combination of theoretical lectures and hands-on coding projects, students will learn key concepts in RL, including MDPs, dynamic programming, deep RL, and the latest advances in model-based and model-free RL algorithms.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECE 612 [Min Grade: C] or CS 510 [Min Grade: C]