The advancements and applications of deep reinforcement learning in Go

Abstract

Combining Deep Learning's perceptual skills with Reinforcement Learning's decision-making abilities, Deep Reinforcement Learning (DRL) represents a significant breakthrough in Artificial Intelligence (AI). This paper examines the evolution and uses of Deep Reinforcement Learning (DRL), emphasizing both the theoretical underpinnings and the noteworthy real-world applications—like AlphaGo's triumph over elite Go players—of the technology. DRL systems learn optimal policies through interactions with their environment, maximizing long-term cumulative rewards. DRL has achieved remarkable results in complex decision-making tasks through the combination of deep learning models like Convolutional Neural Networks (CNN) and reinforcement learning techniques. DRL's potential to transform AI applications is demonstrated by its success in a number of industries, including robotics, autonomous driving, and video games. AlphaGo's success, leveraging DRL and Monte Carlo Tree Search (MCTS), exemplifies the impact of this method on game theory and strategic decision-making. This paper aims to explore the key concepts of DRL, its historical evolution, and its future prospects in advanced AI research.