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• W4287758380 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." @default.
• W4287758380 created "2022-07-26" @default.
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• W4287758380 date "2020-06-12" @default.
• W4287758380 modified "2023-10-14" @default.
• W4287758380 title "Learning to Communicate Using Counterfactual Reasoning" @default.
• W4287758380 doi "https://doi.org/10.48550/arxiv.2006.07200" @default.
• W4287758380 hasPublicationYear "2020" @default.
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