THE ROLE OF DEEP REINFORCEMENT LEARNING IN ROBOTICS AND GAMING

Abstract

Deep Reinforcement Learning (DRL) integrates reinforcement learning with deep neural networks to enable intelligent agents to learn from high-dimensional, sequential data. This paradigm has witnessed remarkable success in diverse application areas, particularly in robotics and gaming. In robotics, DRL enables autonomous skill acquisition, adaptive control, and sim-to-real policy transfer. In gaming, it facilitates superhuman gameplay, advanced game testing, and intelligent non-player character (NPC) behavior. This paper provides a comprehensive overview of the role of DRL in these two domains, discusses current methodologies and their implications, and outlines key challenges and future research directions. In recent years, Deep Reinforcement Learning (DRL) has emerged as a transformative technology, blending the decision-making abilities of reinforcement learning with the powerful pattern recognition capabilities of deep learning. Its influence is particularly notable in two dynamic domains: robotics and gaming. DRL is not just advancing the state-of-the-art in these fields but also redefining how machines learn, adapt, and interact with complex environments.