Reinforcement learning (RL) agents are increasingly being deployed in complex three-dimensional environments. These environments often present challenging obstacles for RL techniques due to the increased degrees of freedom. Bandit4D, a robust new framework, aims to address these hurdles by providing a efficient platform for implementing RL solution