Abstract Interactions among agents are complicated since in order to make the best decisions, each agent has to take into account not only the strategy used by other agents but also how those strategies might change in the future (and what causes these changes). The objective of my work will be to develop a framework for learning agent models (opponent or teammate) more accurately and with less interactions, with a special focus on fast learning non-stationary strategies. As preliminary work we have proposed an initial approach for learning nonstationary strategies in repeated games. We use decision trees to learn a model of the agent, and we transform the learned trees into a MDP and solve it to obtain the optimal policy.