Reinforcement learning (RL) is a field of AI in which actions are taken based upon a states, transitions, expected rewards and other available information. However, when the states and action spaces are not discrete or finite, RL needs to be reformulated and other methods can be applied. In this presentation, I will talk about some of these changes and methods that are used for learning in continuous state-action spaces, and their application to robot motion learning.
Adrià Colomé is a postdoctoral researcher at the Institut de Robòtica i Informàtica Industrial. He focused his PhD in robot motion learning from several perspectives, such as robot kinematics, robot dynamics, reinforcement learning in latent spaces, sample efficiency for robot learning, and learning robot motion adaptability. Currently, he is working towards the challenging topic of learning to manipulate cloth.