Lecturer: Sergio Guadarrama is a senior software engineer at Google Brain. He focuses on reinforcement learning and neural networks. His research focuses on robust, scalable and efficient reinforcement learning. He is currently the leader of the TF-Agents project and a lead developer of TensorFlow (co-creator of TF-Slim). Before joining Google, he was a researcher at the University of California, Berkeley, where he worked with Professor Lotfi Zadeh and Professor Trevor Darrell. He received his B.A. and Ph.D. from the Universidad Politécnica de Madrid.
Abstract: Learning by reinforcement (RL) is a type of machine learning where the objective is to learn to solve a task through interaction with the environment, maximizing the expected return. Unlike supervised learning, the solution requires making multiple decisions in a sequential way and the reinforcement occurs through rewards. The two main components are the environment, which represents the problem to be solved, and the agent, which represents the learning algorithm.