Modern General Game Playing with Reinforcement Learning

Abstract

In computational science and robotics, game playing and artificial intelligence (AI) are research areas that have long been studied. RL systems represent a major step towards autonomous systems that comprehend the visual world at an incredibly deep level and are poised to revolutionize the field of artificial intelligence (AI). Game Play is the testbench where environmental variables can be evaluated to generate adaptive, intelligent, or responsive behavior or simulate real-world scenarios. In this survey, we will explore general gameplay (GGP) with reinforcement learning and its various applications. This survey will cover central algorithms in deep RL, including the deep Qnetwork (DQN) and general mathematical and pragmatic approaches taken in the field. The objective of the current review is to examine the history, associations, and recent advances in the field of general game playing with reinforcement learning and its subfields. We conclude by describing several current areas of research within this field.