# Multi-Agent Reinforcement Learning Simulation for Environmental Policy Synthesis

arXiv: https://arxiv.org/abs/2504.12777 · HTML: https://mlsanity.com/paper/2504.12777/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 60 references that are in the map.
- First posted on arXiv 17 Apr 2025.

## Builds on (most cited first, 13 of 13)

- [Randomized Prior Functions for Deep Reinforcement Learning](https://mlsanity.com/paper/1806.03335/) (2018)
- [Behaviour Suite for Reinforcement Learning](https://mlsanity.com/paper/1908.03568/) (2019)
- [Inequity aversion improves cooperation in intertemporal social dilemmas](https://mlsanity.com/paper/1803.08884/) (2018)
- [Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions](https://mlsanity.com/paper/2003.08536/) (2020)
- [Simulation Intelligence: Towards a New Generation of Scientific Methods](https://mlsanity.com/paper/2112.03235/) (2021)
- [Consequentialist conditional cooperation in social dilemmas with imperfect information](https://mlsanity.com/paper/1710.06975/) (2017)
- [AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-N](https://mlsanity.com/paper/2208.07004/) (2022)
- [Relational Forward Models for Multi-Agent Learning](https://mlsanity.com/paper/1809.11044/) (2018)
- [N-Agent Ad Hoc Teamwork](https://mlsanity.com/paper/2404.10740/) (2024)
- [Deep Gaussian Process Emulation using Stochastic Imputation](https://mlsanity.com/paper/2107.01590/) (2021)
- [Efficient Exploration via Epistemic-Risk-Seeking Policy Optimization](https://mlsanity.com/paper/2302.09339/) (2023)
- [Ensemble Value Functions for Efficient Exploration in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2302.03439/) (2023)
- [Crafting Desirable Climate Trajectories with RL Explored Socio-Environmental Simulations](https://mlsanity.com/paper/2410.07287/) (2024)

## Built on by (most cited first, 3 of 3)

- [Climate Surrogates for Scalable Multi-Agent Reinforcement Learning: A Case Study with CICERO-SCM](https://mlsanity.com/paper/2510.07971/) (2025)
- [LLM Agents Make Collective Belief Dynamics Programmable: Challenges and Research Directions](https://mlsanity.com/paper/2605.19915/) (2026)
- [On Distributional Reinforcement Learning in Chaotic Dynamical Systems](https://mlsanity.com/paper/2605.30160/) (2026)

## Abstract (arXiv)

Climate policy development faces significant challenges due to deep uncertainty, complex system dynamics, and competing stakeholder interests. Climate simulation methods, such as Earth System Models, have become valuable tools for policy exploration. However, their typical use is for evaluating potential polices, rather than directly synthesizing them. The problem can be inverted to optimize for policy pathways, but the traditional optimization approaches often struggle with non-linear dynamics, heterogeneous agents, and comprehensive uncertainty quantification. We propose a framework for augmenting climate simulations with Multi-Agent Reinforcement Learning (MARL) to address these limitations. We identify key challenges at the interface between climate simulations and the application of MARL in the context of policy synthesis, including reward definition, scalability with increasing agents and state spaces, uncertainty propagation across linked systems, and solution validation. Additionally, we discuss challenges in making MARL-derived solutions interpretable and useful for policy-makers. Our framework provides a foundation for more sophisticated climate policy exploration while acknowledging important limitations and areas for future research.