Reinforcement learning (RL) agents are increasingly being deployed in complex spatial environments. These spaces often present novel obstacles for RL methods due to the increased dimensionality. Bandit4D, a powerful new framework, aims to address these challenges by providing a comprehensive platform for implementing RL agents in 3D scenarios. Its