Learning Existing Social Conventions via Observationally Augmented Self-Play
This addresses the challenge of enabling AI agents to integrate smoothly into human social systems, though it is incremental as it builds on existing MARL and imitation learning methods.
The paper tackles the problem of artificial agents failing to coordinate with existing human conventions when trained solely via multi-agent reinforcement learning (MARL), and shows that augmenting MARL with a small amount of imitation learning greatly increases the probability of fitting with real group conventions in environments like traffic, communication, and team coordination.
In order for artificial agents to coordinate effectively with people, they must act consistently with existing conventions (e.g. how to navigate in traffic, which language to speak, or how to coordinate with teammates). A group's conventions can be viewed as a choice of equilibrium in a coordination game. We consider the problem of an agent learning a policy for a coordination game in a simulated environment and then using this policy when it enters an existing group. When there are multiple possible conventions we show that learning a policy via multi-agent reinforcement learning (MARL) is likely to find policies which achieve high payoffs at training time but fail to coordinate with the real group into which the agent enters. We assume access to a small number of samples of behavior from the true convention and show that we can augment the MARL objective to help it find policies consistent with the real group's convention. In three environments from the literature - traffic, communication, and team coordination - we observe that augmenting MARL with a small amount of imitation learning greatly increases the probability that the strategy found by MARL fits well with the existing social convention. We show that this works even in an environment where standard training methods very rarely find the true convention of the agent's partners.