Deep Reinforcement Learning

3 Credits · Level: Undergraduate

Deep reinforcement learning is a machine learning area that learns how to make optimal decisions from interacting with an environment using deep neural networks. An intelligent agent observes the consequences of its action from the environment and alters its behavior to maximize the expected return. We study algorithms and applications in deep reinforcement learning. Topics include Deep neural networks, Markov decision processes, policy gradient methods, Q-Learning (DQN), Actor-Critic, Imitation Learning, and other advanced topics. The course has lectures, readings, programming assignments, and exams.