Tutorial: Advances in Deep Reinforcement Learning

This tutorial presents in detail the current state-of-the-art deep RL theory and applications. We highlight the differences between various types of deep RL methods and their corresponding applications. We present our recent algorithms in the multi-objective and multi-agent domains. Real-world examples for each type of deep RL methods are given along with their demonstrations. This tutorial offers a unique opportunity to disseminate in-depth knowledge on deep RL and how to use those algorithms to solve real-world problems such as autonomous vehicles (cars and drones), autonomous surgical robotics, applications in finance, cybersecurity, and the Internet of Things.