Evaluation & Benchmarking

A standardized library for benchmarking multi-agent reinforcement learning algorithms across environments and reproducible experiment configurations.

A standard API and maintained collection of environments for research and evaluation in multi-agent reinforcement learning.

The StarCraft Multi-Agent Challenge environment for evaluating cooperative multi-agent reinforcement learning methods.

A research framework for cooperative multi-agent reinforcement learning algorithms and experiments built around SMAC.

A suite of social multi-agent reinforcement learning scenarios for evaluating cooperation, competition, and generalization.

A vectorized differentiable simulator providing fast multi-robot scenarios for multi-agent reinforcement learning research.

The official research implementation of a transformer-based architecture for cooperative multi-agent reinforcement learning.

A historical set of lightweight particle environments for cooperative and competitive multi-agent reinforcement learning.