Martin Riedmiller is a research scientist and team-lead at DeepMind, London. Before joining DeepMind fulltime in spring 2015, he held several professor positions in machine learning and neuro-informatics from 2002 to 2015 at Dortmund, Osnabrück and Freiburg University. From 1998 to 2009 he lead the robot soccer team ‘Brainstormers’ that participated in the internationally renowned RoboCup competitions. As an early proof of the power of neural reinforcement learning techniques, the Brainstormers won the world championships for five times in both simulation and real robot leagues. He has contributed over 20 years in the fields of reinforcement learning, neural networks and learning control systems. He is author and co-author of some early and ground-lying work on efficient and robust supervised learning and reinforcement learning algorithms, including work on one of the first deep reinforcement learning systems.
Collect & Infer: how to efficiently learn control
Being able to autonomously learn control with minimal prior knowledge is a key ability of intelligent systems. A particular challenge in real world control scenarios are methods that are at the same time highly data-efficient and robust, since data-collection on real systems is time intensive and often expensive. I will discuss the collect & infer paradigm for Reinforcement Learning, that takes a fresh look at data collection and exploitation in data-efficient agents. I will give examples of learning agent designs that can learn increasingly complex tasks from scratch in simulation and reality.