Reinforcement learning (RL) models are increasingly being deployed in complex three-dimensional environments. These environments often present challenging obstacles for RL methods due to the increased dimensionality. Bandit4D, a powerful new framework, aims to mitigate these hurdles by providing a efficient platform for implementing RL agents in 3D