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