Learning to Communicate Using Counterfactual Reasoning
This work addresses communication challenges in multi-agent systems, offering incremental improvements for researchers in MARL.
The paper tackles the problem of learning communication protocols in multi-agent reinforcement learning by addressing credit assignment, non-stationarity, and agent influenceability, resulting in a method that outperforms state-of-the-art baselines in four Particle environment scenarios.
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.