# Exploring Equity of Climate Policies using Multi-Agent Multi-Objective Reinforcement Learning

arXiv: https://arxiv.org/abs/2505.01115 · HTML: https://mlsanity.com/paper/2505.01115/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 48 references that are in the map.
- First posted on arXiv 2 May 2025.

## Builds on (most cited first, 7 of 7)

- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [A Practical Guide to Multi-Objective Reinforcement Learning and Planning](https://mlsanity.com/paper/2103.09568/) (2021)
- [Multi-Objective Multi-Agent Decision Making: A Utility-based Analysis and Survey](https://mlsanity.com/paper/1909.02964/) (2019)
- [Multi-Objective Reinforcement Learning Based on Decomposition: A Taxonomy and Framework](https://mlsanity.com/paper/2311.12495/) (2023)
- [AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-N](https://mlsanity.com/paper/2208.07004/) (2022)
- [MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2407.16312/) (2024)
- [Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective Reinforcement Learning](https://mlsanity.com/paper/2402.02665/) (2024)

## Built on by (most cited first, 2 of 2)

- [Climate Surrogates for Scalable Multi-Agent Reinforcement Learning: A Case Study with CICERO-SCM](https://mlsanity.com/paper/2510.07971/) (2025)
- [Position: AI Must Become Planet-Centered, Not Just Human-Centered](https://mlsanity.com/paper/2606.13704/) (2026)

## Abstract (arXiv)

Addressing climate change requires coordinated policy efforts of nations worldwide. These efforts are informed by scientific reports, which rely in part on Integrated Assessment Models (IAMs), prominent tools used to assess the economic impacts of climate policies. However, traditional IAMs optimize policies based on a single objective, limiting their ability to capture the trade-offs among economic growth, temperature goals, and climate justice. As a result, policy recommendations have been criticized for perpetuating inequalities, fueling disagreements during policy negotiations. We introduce Justice, the first framework integrating IAM with Multi-Objective Multi-Agent Reinforcement Learning (MOMARL). By incorporating multiple objectives, Justice generates policy recommendations that shed light on equity while balancing climate and economic goals. Further, using multiple agents can provide a realistic representation of the interactions among the diverse policy actors. We identify equitable Pareto-optimal policies using our framework, which facilitates deliberative decision-making by presenting policymakers with the inherent trade-offs in climate and economic policy.