How can we teach artificial agents to use human language flexibly to solve problems in a real-world environment? We have one example in nature of agents being able to solve this problem: human babies eventually learn to use human language to solve problems, and they are taught with an adult human-in-the-loop. Unfortunately, current machine learning methods (e.g. from deep reinforcement learning) are too data inefficient to learn a language in this way (3). An outstanding goal is finding an algorithm with a suitable ‘language learning prior’ that allows it to learn human language, while minimizing the number of required human interactions.

 In this paper, we propose to learn such a prior in simulation, leveraging the increasing amount of available compute for machine learning experiments (1). We call our approach Learning to Learn to Communicate (L2C). Specifically, in L2C we train a meta-learning agent in simulation to interact with populations of pre-trained agents, each with their own distinct communication protocol. Once the meta-learning agent is able to quickly adapt to each population of agents, it can be deployed in new populations unseen during training, including populations of humans. To show the promise of the L2C framework, we conduct some preliminary experiments in a Lewis signaling game (4), where we show that agents
 trained with L2C are able to learn a simple form of human language (represented by a hand-coded compositional language) in fewer iterations than randomly initialized agents.
We propose to use meta-learning for more efficient language learning, via a kind of 'domain randomization'. 