  This paper proposes enhancements to the RICE-N simulation and multi-agent
reinforcement learning framework to improve the realism of international
climate policy negotiations. Acknowledging the framework's value, we highlight
the necessity of significant enhancements to address the diverse array of
factors in modeling climate negotiations. Building upon our previous work on
the "Conditional Commitments Mechanism" (CCF mechanism) we discuss ways to
bridge the gap between simulation and reality. We suggest the inclusion of a
recommender or planner agent to enhance coordination, address the Real2Sim gap
by incorporating social factors and non-party stakeholder sub-agents, and
propose enhancements to the underlying Reinforcement Learning solution
algorithm. These proposed improvements aim to advance the evaluation and
formulation of negotiation protocols for more effective international climate
policy decision-making in Rice-N. However, further experimentation and testing
are required to determine the implications and effectiveness of these
suggestions.
