Body Language Analysis Based on Deep Reinforcement Learning

GCTB-NSU Joint Institute of Technology, Guangzhou College of Technology and Business, Foshan, Guangdong, China

luboyang@gzgs.edu.cn

Abstract

Deep learning has become one of the core technologies in current artificial intelligence research and application and has triggered revolutionary breakthroughs in many fields, demonstrating powerful learning ability and creativity. With the introduction of various Deep Reinforcement Learning (DRL) algorithms, learning of different tasks has become more targeted and efficient. This paper focuses on the combination of deep reinforcement learning and body language analysis. It discusses in detail the three mainstream reinforcement learning algorithms: Deep Q Network (DQN), Proximal Policy Optimization (PPO) and Asynchronous Advantage Actor-Critic (A3C), and makes an in-depth comparison of their applicability in body language analysis tasks. The performance of each algorithm is evaluated through experimental results, and finally, the optimal algorithm that is more suitable for body language analysis is selected. Through in-depth analysis of the experimental results, the advantages and limitations of different algorithms in this task are revealed, providing valuable reference and inspiration for the application of deep reinforcement learning in the field of body language analysis