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