Reinforcement learning (RL) agents are increasingly being deployed in complex three-dimensional environments. These environments often present novel obstacles for RL methods due to the increased degrees of freedom. Bandit4D, a robust new framework, aims to address these limitations by providing a efficient platform for training RL systems in 3D sim