  In scenarios where a single player cannot control other players, cooperative
AI is a recent technology that takes advantage of deep learning to assess
whether cooperation might occur. One main difficulty of this approach is that
it requires a certain level of consensus on the protocol (actions and rules),
at least from a majority of players. In our work, we study the simulations
performed on the cooperative AI tool proposed in the context of AI for Global
Climate Cooperation (AI4GCC) competition. We experimented simulations with and
without the AI4GCC default negotiation, including with regions configured
slightly differently in terms of labor and/or technology growth. These first
results showed that the AI4GCC framework offers a promising cooperative
framework to experiment with global warming mitigation. We also propose future
work to strengthen this framework.
