Courses Detail Information

ECE6705J – Deep Reinforcement Learning


Instructor: Paul Weng

Instructors (Faculty):

Credits: 3 credits

Pre-requisites: Necessary computer science and mathematical background covered in VE281, VE203, VV216/256/286

Description:

Reinforcement learning is a generic artificial intelligence approach for autonomously learning from trials and error. Deep reinforcement learning is the integration of deep learning and reinforcement learning. This course will cover the main recent algorithms and approaches in deep reinforcement learning and illustrate them on various applications (traffic light control, video games, robotics…).
This course provides a presentation of deep reinforcement learning (RL), with a focus on its recent developments. Topics include RL basics, deep Q-learning, policy gradients, actor-critic algorithms, model-based RL, imitation learning, inverse RL, hierarchical RL, and multi-agent RL.

Course Topics:

Sample Syllabus