Reinforcement learning (RL) agents are increasingly being deployed in complex spatial environments. These spaces often present novel difficulties for RL methods due to the increased complexity. Bandit4D, a powerful new framework, aims to overcome these challenges by providing a flexible platform for implementing RL agents in 3D simulations. Its ada