Reinforcement learning (RL) agents are increasingly being deployed in complex three-dimensional environments. These environments often present novel obstacles for RL algorithms due to the increased complexity. Bandit4D, a powerful new framework, aims to mitigate these hurdles by providing a flexible platform for developing RL agents in 3D simulatio