Reinforcement learning (RL) agents are increasingly being deployed in complex three-dimensional environments. These spaces often present novel problems for RL algorithms due to the increased degrees of freedom. Bandit4D, a powerful new framework, aims to mitigate these challenges by providing a efficient platform for developing RL agents in 3D worl