Deep Reinforcement Learning

Deep Reinforcement Learning is a course that explores the intersection of machine learning and artificial intelligence with decision-making processes. The course is designed to provide students with a comprehensive understanding of reinforcement learning, a powerful subset of machine learning, and how it can be leveraged to build intelligent systems that can learn from experience. The course begins with an overview of the basic concepts of reinforcement learning, including the Markov decision process, the value function, and the policy. It then progresses to cover more advanced topics, including deep reinforcement learning, which is the use of deep neural networks to learn complex decision-making tasks. The course covers a wide range of topics related to deep reinforcement learning, including policy gradient methods, actor-critic methods, and Q-learning. Students will learn how to use various deep learning techniques to solve real-world problems, such as autonomous driving, game playing, and robotics. The course also covers practical applications, such as how to train a neural network to play a game like Atari or to control a robot in a simulated environment. Students will learn how to implement various algorithms using popular deep learning frameworks like TensorFlow, PyTorch, and Keras. Throughout the course, students will work on several projects to apply what they have learned. These projects will involve implementing deep reinforcement learning algorithms to solve various tasks, such as training a model to play a game or to control a robotic arm. By the end of the course, students will have a solid understanding of the fundamental concepts and how to apply them to solve real-world problems. They will have gained experience working with popular deep learning frameworks and will be well-equipped to continue exploring the field of reinforcement learning on their own. Overall, This course is an exciting and challenging course that offers students the opportunity to learn about the cutting-edge techniques and applications of reinforcement learning in artificial intelligence. Author: Sergey Levine