To address limitations of real-time and local conflict coordination for multi-objective path planning in Virtual Reality （VR） scenes， a multi?agent path planning algorithm based on hierarchical adaptive implicit quantile was proposed. First， collaborative optimization of high- and low-level strategies was introduced to unify global planning and local execution， and an implicit quantile network was employed for value distribution modeling. The high-level strategy was used to generate sub-goals considering both global environmental distribution and target positions， while the low-level strategy was used to perceive local environmental dynamics according to sub-goals in real time. Then， an adaptive Conditional Value at Risk （CVaR） mechanism was used to dynamically adjust decisions. Finally， a goal-smooth reward function was designed to guide agents toward rapid and stable target achievement by optimizing feedback structure of the strategy. Experimental results show that the proposed algorithm outperforms the suboptimal algorithm， adaptive_IQN （Adaptive Implicit Quantile Network）， by 2， 3， 3， and 4 percentage points under scenarios with 3 agents and 5， 10， 15， and 20 targets， respectively， optimizing path planning efficiency and enhancing fluency and immersion of multi-user interactions in VR scenes.