Learning to Communicate with Strangers via Channel Randomisation Methods
This addresses the challenge of enabling AI agents to communicate effectively with strangers in collaborative tasks, though it appears incremental as it builds on existing self-play and communication protocol frameworks.
The paper tackled the problem of improving zero-shot communication performance between agents meeting for the first time by introducing message mutation and channel permutation methods, finding that both positively influenced performance in a two-player teacher-student game.
We introduce two methods for improving the performance of agents meeting for the first time to accomplish a communicative task. The methods are: (1) `message mutation' during the generation of the communication protocol; and (2) random permutations of the communication channel. These proposals are tested using a simple two-player game involving a `teacher' who generates a communication protocol and sends a message, and a `student' who interprets the message. After training multiple agents via self-play we analyse the performance of these agents when they are matched with a stranger, i.e. their zero-shot communication performance. We find that both message mutation and channel permutation positively influence performance, and we discuss their effects.