Improved empirical methods in reinforcement-learning evaluation

@inproceedings{Marivate2015ImprovedEM,
  title={Improved empirical methods in reinforcement-learning evaluation},
  author={Vukosi N. Marivate and Michael L. Littman},
  year={2015}
}
OF THE DISSERTATION IMPROVED EMPIRICAL METHODS IN REINFORCEMENT-LEARNING EVALUATION by VUKOSI N. MARIVATE Dissertation Director: Michael L. Littman The central question addressed in this research is ”can we define evaluation methodologies that encourage reinforcement-learning (RL) algorithms to work effectively with real-life data?” First, we address the problem of overfitting. RL algorithms are often tweaked and tuned to specific environments when applied, calling into question whether… CONTINUE READING