Melting Pot: an evaluation suite for multi-agent reinforcement learning
Here we introduce Melting Pot, a scalable evaluation suite for multi-agent reinforcement learning. Melting Pot assesses generalization to novel social situations involving both familiar and unfamiliar individuals, and has been designed to test a broad range of social interactions such as: co-operation, competition, deception, reciprocation, trust, stubbornness and so on. Melting Pot offers researchers a set of 21 MARL “substrates” (multi-agent games) on which to train agents, and over 85 unique test scenarios on which to evaluate them. The resulting score can then be used to rank different multi-agent RL algorithms by their ability to generalise to novel social situations. We hope Melting Pot will become a standard benchmark for multi-agent reinforcement learning. We plan to maintain it, and will be extending it in the coming years to cover more social interactions and generalisation scenarios.
See the Melting Pot GitHub page for instructions on how to use it.