openai/multi-agent-emergence-environments

Health v2 · maintenance only

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1814 stars · 325 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

OpenAI's environment generation code for multi-agent reinforcement learning research, implementing environments like Hide and Seek from the paper 'Emergent Tool Use From Multi-Agent Autocurricula'. It is built on MuJoCo and mujoco-worldgen, providing composable EnvModules and Gym wrappers for constructing multi-agent physics-based games.

Use cases

• reproduce the hide and seek multi-agent experiments from the OpenAI paper
• build custom multi-agent physics environments with boxes, ramps, and walls
• study emergent tool use and autocurricula in multi-agent RL
• run saved policies in hide and seek environments for analysis
• construct transfer tasks like lock and return or blueprint construction

When to choose

• you need the exact environments from the Emergent Tool Use paper for reproduction or follow-up research
• you want a modular MuJoCo-based framework for composing multi-agent environments
• you are studying multi-agent autocurricula and emergent behaviors

When to avoid

• you need maintained software with active support or bug fixes
• you want modern Python versions beyond 3.6 or Windows support
• you need lightweight or non-physics multi-agent environments
• you are looking for a production-ready RL training framework rather than research environments

Facets

library · maturity abandoned

simulation machine-learning reinforcement-learning reinforcement-learning artificial-intelligence simulation python multi-agent mujoco gym-environments emergent-behavior research-code archived research linux macos

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markdown · JSON · MCP: product_card(name="openai/multi-agent-emergence-environments")