Courses Detail Information
VE693 – Deep Reinforcement Learning
Instructor: Paul Weng
Credits: 3 credits
Pre-requisites: Necessary computer science and mathematical background covered in VE281, VE203, VV216/256/286
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.