Action advice is an important mechanism to improve the learning speed of multiple agents. To do so, an advisor agent suggests actions to an advisee agent. In the current advising approaches, the advisor’s advice is always applicable based on the assumption that the advisor and advisee have the same objective, and the environment is stable. However, in many real-world applications, the advisor and advisee may have different objectives, and the environment may be dynamic. This would make the advisor’s advice not always applicable. In this paper, we propose an approach where the advisor and advisee jointly determine the applicability of advice by considering the different objectives and dynamic changes in the environment. The proposed approach is evaluated in various robot navigation domains. The evaluation results show that the proposed approach can determine the applicability of advice. The multi-agent learning speed can also be improved benefiting from determined applicable advice.