Description
This course introduces the core principles and algorithms of Reinforcement Learning, focusing on how agents learn optimal behavior through interaction with an environment to maximize cumulative rewards. Students will build a foundation in Markov decision processes, dynamic programming, Monte Carlo methods, and temporal-difference learning. The course also covers topics such as exploration strategies, multi-armed bandits, policy gradient methods, Deep Reinforcement Learning, and Reinforcement Learning from Human Feedback. Pre-requisites: (GNE331 or MTH305) and (COE211 or COE212) and fourth-year standing.
Common questions about COE 550
What is COE 550 at LAU?
COE 550 Reinforcement Learning is a 3-credit course at Lebanese American University (LAU), in the Electrical & Computer Eng. department. This course introduces the core principles and algorithms of Reinforcement Learning, focusing on how agents learn optimal behavior through interaction with an environment to maximize cumulative rewards.
How many credits is COE 550?
COE 550 Reinforcement Learning is worth 3 credits at LAU.
Who teaches COE 550 at LAU?
Studiety has 1 professor on record for COE 550: Fouad Antonios Trad. Sections change every term, so check the registrar for who is teaching now.