Reinforcement learning (RL) agents are increasingly being deployed in complex spatial environments. These scenarios often present challenging problems for RL algorithms due to the increased dimensionality. Bandit4D, a robust new framework, aims to mitigate these challenges by providing a flexible platform for implementing RL agents in 3D simulation