Reinforcement Learning and Dynamic Programming Using Function Approximators

From household appliances to applications in robotics, engineered systems involving complex dynamics can only be as effective as the algorithms that control them. While Dynamic Programming (DP) has provided researchers with a way to optimally solve decision and control problems involving complex dynamic systems, its practical value was limited by algorithms that lacked the capacity to scale up to realistic problems.  However, in recent years, dramatic developments in Reinforcement Learning (RL), the model-free counterpart of DP, changed our understanding of what is possible. Those developments led to the creation of reliable methods that can be applied even when a mathematical model of the system is…

• FD
  Fonds De La Recherche Scientifique - FNRS
• NO
  Nederlandse Organisatie voor Wetenschappelijk Onderzoek
• UD
  Université de Liège