Deep Reinforcement Learning is an advanced course that explores the intersection of artificial intelligence, machine learning, and control systems. This course focuses on teaching students the principles and techniques of reinforcement learning (RL) with an emphasis on deep learning algorithms. Reinforcement learning is a subfield of machine learning that involves training agents to make sequential decisions in an environment to maximize a reward signal. Deep reinforcement learning extends this concept by incorporating deep neural networks to handle high-dimensional input spaces and complex decision-making tasks.Throughout this course, students will delve into the theoretical foundations and practical applications of deep reinforcement learning. They will gain a comprehensive understanding of fundamental RL concepts, including Markov decision processes, value functions, policy optimization, and exploration-exploitation trade-offs.
