Reinforcement learning (RL) models are increasingly being deployed in complex spatial environments. These scenarios often present challenging obstacles for RL techniques due to the increased complexity. Bandit4D, a robust new framework, aims to mitigate these hurdles by providing a comprehensive platform for implementing RL agents in 3D worlds. Its