Efficient reinforcement learning in parameterized models: discrete parameters
Kirill Dyagilev, Shie Mannor, Nahum Shimkin · 2008
We consider reinforcement learning in a parameterized setup, where the controlled model is known to belong to a finite set of Markov Decision Processes (MDPs) under the discounted return criteria. We propose an on-line algorithm for learning in such parameterized models, called the Parameter Elimina