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arXiv · 2006.07200

Learning to Communicate Using Counterfactual Reasoning

Simon Vanneste·Astrid Vanneste·Kevin Mets·Tom De Schepper·Ali Anwar·Siegfried Mercelis·Steven Latré·Peter Hellinckx
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Abstract

Learning to communicate in order to share state information is an active problem in the area of multi-agent reinforcement learning (MARL). The credit assignment problem, the non-stationarity of the communication environment and the creation of influenceable agents are major challenges within this research field which need to be overcome in order to learn a valid communication protocol. This paper introduces the novel multi-agent counterfactual communication learning (MACC) method which adapts counterfactual reasoning in order to overcome the credit assignment problem for communicating agents. Secondly, the non-stationarity of the communication environment while learning the communication Q-function is overcome by creating the communication Q-function using the action policy of the other agents and the Q-function of the action environment. Additionally, a social loss function is introduced in order to create influenceable agents which is required to learn a valid communication protocol. Our experiments show that MACC is able to outperform the state-of-the-art baselines in four different scenarios in the Particle environment.

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Simon Vanneste, Astrid Vanneste, Kevin Mets, Tom De Schepper, Ali Anwar, Siegfried Mercelis, Steven Latré, Peter Hellinckx. 2020-06-12. Learning to Communicate Using Counterfactual Reasoning. https://arxiv.org/abs/2006.07200

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