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.

通过增强 Rice-N 模拟和多智能体强化学习框架来提高国际气候政策谈判的真实性，并通过纳入社会因素和非政府利益相关方子智能体来弥合模拟与现实之间的差距，同时提出了强化学习算法的改进，旨在更有效地评估和制定谈判协议以促进国际气候政策决策的发展，但需要进一步实验和测试来确定这些建议的影响和效果。