Reinforcement learning is a general paradigm for learning to act under uncertainty, and it is applicable to a wide range of tasks, including robotics, game playing, user-interactive systems (e.g., recommender systems), and healthcare.

This course covers the fundamentals of reinforcement learning and its practices. The course aims to provide hands-on experiences in the algorithmic techniques (model-based, model-free, policy gradients, etc.) of reinforcement learning. The students will be well-versed in both the fundamental principles of RL and the implementation of (deep) RL algorithms.