Better cooperation through communication in multi-agent reinforcement learning

Abstract: Cooperative needs play a critical role in the organisation of natural systems of communications. A number of recent studies in multi-agent reinforcement learning have established that artificial intelligence agents are similarly able to develop functional communication when required to complete a cooperative task. This thesis studies the emergence of communication in reinforcement learning agents, using a custom card game environment as a test-bed. Two contrasting approaches encompassing continuous and discrete modes of communication were appraised experimentally. Based on the average game completion rate, the agents provisioned with a continuous communication channel consistently exceed the no-communication baseline. A qualitative analysis of the agents’ behavioural strategies reveals a clearly defined communication protocol as well as the deployment of playing tactics unseen in the baseline agents. On the other hand, the agents equipped with the discrete channel fail to learn to utilise it effectively, ultimately showing no improvement from the baseline.