Deep reinforcement learning is now a potent tool for building intelligent agents that excel in challenging strategic games. Chess, a well- liked board game with lots of room for exploration, has been utilized to test DRL algorithms. The game of chess is widely recognized for its deep strategic complexity, extensive history, and complex rules. In this paper, we explore the application of DRL in this game. We investigate the use of neural networks, such as recurrent neural networks (RNN) and convolutional neural networks (CNN), in conjunction with reinforcement learning algorithms, such as Proximal Policy Optimization (PPO), Deep Q-networks (DQN), and others, to construct highly performing game playing agents. Our research investigates the survey of multiple research papers concerning this topic and examines how DRL can be applied in chess.