Reinforcement learning (RL) agents are increasingly being deployed in complex 3D environments. These spaces often present novel obstacles for RL algorithms due to the increased degrees of freedom. Bandit4D, a powerful new framework, aims to mitigate these limitations by providing a efficient platform for training RL systems in 3D scenarios. Its sca