Date: Tue. September 8 – Thu. October 1, 2020
Organized by: Deusto CCM
Title: CCM Course: Inverse problems in Reinforcement Learning
Speaker: Dr. Carlos Esteve Yague,
Affiliation: UAM | Deusto CCM
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.
Slides
- Introduction and Dynamic Programming Methods
- From discrete to continuous models, Hamilton-Jacobi-Bellman equations
- Q-Learning
- Inverse problems in Reinforcement Learning
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