Multi-agent reinforcement learning (MARL) makes agents cooperate with each other by reinforcement learning to achieve collective action. Generally, MARL enables agents to predict the unknown factor of other agents in reward function to achieve obtaining maximize reward cooperatively, then it is important to diminish the complexity of communication or observation between agents to achieve the cooperation, which enable it to real-world problems. By contrast, this paper proposes an implicit cooperative learning (ICL) that have an agent separate three factors of self-agent can increase, another agent can increase, and interactions influence in a reward function approximately, and estimate a reward function for self from only acquired rewards to learn cooperative policy without any communication and observation. The experiments investigate the performance of ICL and the results show that ICL outperforms the state-of-the-art method in two agents cooperation problem.