Organized by CCM – Chair of Computational Mathematics
Speaker: Carlos Esteve Yague, Postdoctoral Researcher at CCM
From September 8th. to October 1st, 2020
Sessions: 4, one session/week
Abstract. This mini-course aims to be an introduction to Reinforcement Learning for people with a background in control theory. We will discuss the differences and similarities between the two settings, relying on Markov decision processes (MDP) and dynamical systems (DS) respectively. We will present and analyze the most elementary Reinforcement Learning techniques, based on the dynamic programming principle. By means of the HJB equation, we will also discuss the possibility of implementing RL methods in continuous settings. Finally, we will consider inverse problems arising in this context, where the goal is to identify the underlying dynamics of the system and/or a cost functional compatible with a given optimal policy.
Sessions planning:
| Date & Room | Session | Subject |
|---|---|
| 08.09.2020 10:00-11:30 Google Meet & Logistar Room at DeustoTech |
Introduction and Dynamic Programming Methods |
| 17.09.2020 12:00-13:00 Google Meet & Logistar Room at DeustoTech |
From discrete to continuous models, Hamilton-Jacobi-Bellman equations |
| 24.09.2020 12:00-13:00 Google Meet & Logistar Room at DeustoTech |
Q-Learning |
| 01.10.2020 12:00-13:00 Google Meet |
Inverse problems in Reinforcement Learning |
