This paper presents a novel theory for learning generic prior models from a set of observed natural images based on a minimax entropy theory that the authors studied in modeling t...
This paper presents insights about design practices that can lead to effective and fun games for learning, gleaned from interviews with experienced game developers. We based our a...
Meta-Learning has been used to select algorithms based on the features of the problems being tackled. Each training example in this context, i.e. each meta-example, stores the feat...
In a typical reinforcement learning (RL) setting details of the environment are not given explicitly but have to be estimated from observations. Most RL approaches only optimize th...
In this paper, we study a sequential decision making problem. The objective is to maximize the total reward while satisfying constraints, which are defined at every time step. The...