Deep reinforcement learning with variable-size human models in virtual reality

In recent years, the integration of deep reinforcement learning (DRL) with virtual reality (VR) has opened new avenues for developing advanced interactive systems, with many Artificial Intelligence (AI) opponents being used in different games. Based on the VR agent we trained previously, we expand on the models and capabilities of the agents. By reflecting the agent’s physical state and capability to as close to the real world as possible, we train agents with different physical heights, different arm lengths and different speeds. By comparing these agents’ strategies to the real-world table tennis players, we can provide a more comprehensive list of AI opponents that is even closer to real humans in VR. Experimental results show that models with different attributes tend to adopt different strategies to win the game, which is of great significance for table tennis training in real life.