  Recently, deep reinforcement learning (RL) algorithms have made great
progress in multi-agent domain. However, due to characteristics of RL, training
for complex tasks would be resource-intensive and time-consuming. To meet this
challenge, mutual learning strategy between homogeneous agents is essential,
which is under-explored in previous studies, because most existing methods do
not consider to use the knowledge of agent models. In this paper, we present an
adaptation method of the majority of multi-agent reinforcement learning (MARL)
algorithms called KnowSR which takes advantage of the differences in learning
between agents. We employ the idea of knowledge distillation (KD) to share
knowledge among agents to shorten the training phase. To empirically
demonstrate the robustness and effectiveness of KnowSR, we performed extensive
experiments on state-of-the-art MARL algorithms in collaborative and
competitive scenarios. The results demonstrate that KnowSR outperforms recently
reported methodologies, emphasizing the importance of the proposed knowledge
sharing for MARL.
