Applications of Reinforcement Learning on Humanoid Robot Controlling

Amidst the swift advancement of artificial intelligence and machine learning technologies, humanoid robots are increasingly demonstrating their potential in mimicking human motions, adapting to intricate environments, and executing a wide array of tasks. Reinforcement Learning (RL), as an advanced learning paradigm that optimizes decision-making processes through environmental interaction, has emerged as a pivotal tool in enhancing the capabilities of humanoid robots. In this paper, the researcher delves into a variety of RL techniques and their implementations, spotlighting key accomplishments and addressing the prevailing challenges alongside envisaging future trajectories. Through an in-depth examination of these applications, our aim is to elucidate the transformative potential of RL in the domain of humanoid robotics, paving the way for more adaptive, intelligent, and autonomous systems. This paper posits that, with the deepening of research and technological progress, RL will catalyze breakthroughs in the humanoid robot sector, propelling smart robots towards greater integration within human society.