Achieving superhuman playing level by AlphaGo corroborated the capabilities of convolutional neural architectures (CNNs) for capturing complex spatial patterns. This result was to a great extent due to several analogies between Go board states and 2D images CNNs have been designed for, in particular translational invariance and a relatively large board. In this paper, we verify whether CNN-base...

Operations research and management science are often confronted with sequential decision making problems with large state spaces. Standard methods that are used for solving such complex problems are associated with some difficulties. As we discuss in this article, these methods are plagued by the so-called curse of dimensionality and the curse of modelling. In this article, we discuss reinforce...

the so-called “third world” has often experienced modernity and its version of the “city” in some of their most grotesque forms where iran has not been an exception. in other words, north-atlantic powers have historically played the midwife for a father-figure-like modernity and its “third world” concubines, the result of which has been the birth of “monstrosities” of all kinds. focusing mainly...

The purpose of reinforcement learning system is to learn optimal policies in general. However, from the engineering point of view, it is useful and important to acquire not only optimal policies, but also penalty avoiding policies. In this paper, we are focused on formation of penalty avoiding policies based on the Penalty Avoiding Rational Policy Making algorithm [1]. In applying the algorithm...