A research environment for multi-agent reinforcement learning, featuring particle-world scenarios with continuous observations and discrete actions. The codebase implements the environments used in the MADDPG paper for studying cooperative and competitive multi-agent interactions. It provides configurable scenarios with landmarks, agents, and simulated physics, packaged as OpenAI Gym-compatible environments.