Provides simulation environments designed for multi-agent reinforcement learning research, supporting complex agent interactions like hide-and-seek and object manipulation. The environment construction system uses modular components (EnvModules and gym.Wrappers) that enable researchers to build and customize agent training scenarios. Supports configurable game variants including random rooms, quadrant, and food scenarios.