Reinforcement learning (RL) systems are increasingly being deployed in complex spatial environments. These spaces often present novel obstacles for RL techniques due to the increased dimensionality. Bandit4D, a cutting-edge new framework, aims to address these limitations by providing a efficient platform for training RL systems in 3D simulations.