Reinforcement learning (RL) systems are increasingly being deployed in complex spatial environments. These spaces often present novel obstacles for RL methods due to the increased complexity. Bandit4D, a robust new framework, aims to address these limitations by providing a flexible platform for training RL solutions in 3D simulations. Its adaptive