Abstract: An agent in a multi-agent system is provided with a policy model that controls communication of the agent with other agents in the multi-agent system. The policy model is trained by using MARL. The policy model receives more messages from one or more other agents in the multi-agent system. The policy model generates a reward score based at least on a hidden state of the agent and the one or more messages. The reward score represents an aggregation of a value of sending the message for a task and a cost of sending the message. The policy model determines whether to send the message based on the reward score. After determining to send the message, the policy model generates the message based on the hidden state of the agent and the one or more messages and sends the message to one or more other agents in the multi-agent system.