Reinforcement Learning for control of continuous-time systems

Farnaz Adib Yaghmaie, Linköping University


Machine learning can be divided into three categories: 1- Supervised learning, 2- Unsupervised learning and 3- Reinforcement Learning (RL). Within these categories, RL is specifically interesting, as it concerns with learning optimal policies from interaction with an environment and receiving a cost. In this sense, RL implies a cause and effect relationship between policies and costs, and as such, RL based frameworks enjoy optimality and adaptivity. In this talk, we consider RL from a control perspective; that is, we consider RL techniques for dynamical systems with continuous state and control-space. This is more demanding in comparison with RL for classical Markov Decision Processes (MDP) with a finite number of state and control variables since the stability of the dynamical systems as well as other control related properties need to be guaranteed.