Title: Reducing Communication Entropy in Multi-Agent Reinforcement Learning. Abstract: Communication in multi-agent reinforcement learning has been drawing attention recently for its significant role in cooperation. However, multi-agent systems may suffer from limitations on communication resource and thus need efficient communication techniques in real-world scenarios. According to the Shannon-Hartley theorem, messages to be transmitted reliably in worse channels requires lower entropy. Therefore, we aim to reduce message entropy in multi-agent communication. A fundamental challenge in this is that the gradients of entropy are either 0 or infinity, disabling gradient-based methods. To handle it, we propose a pseudo gradient descent scheme, which reduces entropy by adjusting the distributions of messages wisely. We conduct experiments on six environment settings and two base communication frameworks and find that our scheme can reduce communication entropy by up to 90% with nearly no loss of performance.